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Abstract

Questions: Vegetation-plot records provide information on presence and cover or abundance of plants co-occurring in the same community. Vegetation-plot data are spread across research groups, environmental agencies and biodiversity research centers, and thus, are rarely accessible at continental or global scales. Here we present the sPlot database, which collates vegetation plots worldwide to allow for the exploration of global patterns in taxonomic, functional and phylogenetic diversity at the plant community level. - Location: sPlot version 2.1 contains records from 1,121,244 vegetation plots, which comprise 23,586,216 records of plant species and their relative cover or abundance in plots collected between 1885 and 2015. - Methods: We complemented the information for each plot by retrieving environmental conditions (i.e. climate and soil) and the biogeographic context (i.e. biomes) from external sources, and by calculating community-weighted means and variances of traits using gap-filled data from the global plant trait database TRY. Moreover, we created a phylogenetic tree for 50,167 out of the 54,519 species identified in the plots. - Results: We present the first maps of global patterns of community richness and community-weighted means of key traits. - Conclusions: The availability of vegetation plot data in sPlot offers new avenues for vegetation analysis at the global scale.
J Veg Sci. 2019;30:161–186. wileyonlinelibrary.com/journal/jvs  
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 161
Journal of Vegetation Science
© 2019 International Association
for Vegetation Science
Received:5October2018 
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  Revised:5N ovember2018 
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  Accepted:15November2018
DOI: 10.1111/jvs.12710
REPORT
sPlot – A new tool for global vegetation analyses
Helge Bruelheide1,2,*| Jürgen Dengler2,3,4,*| Borja Jiménez-Alfaro1,2,5,*|
Oliver Purschke1,2,*| Stephan M. Hennekens6| Milan Chytrý7| Valério
D. Pillar8| Florian Jansen9| Jens Kattge2,10 | Brody Sandel11| Isabelle Aubin12|
Idoia Biurrun13 | Richard Field14 | Sylvia Haider1,2| Ute Jandt1,2|
Jonathan Lenoir15 | Robert K. Peet16 | Gwendolyn Peyre17| Francesco
Maria Sabatini1,2 | Marco Schmidt18 | Franziska Schrodt14 | Marten Winter2|
SvetlanaAćić19| Emiliano Agrillo20 | Miguel Alvarez21 |DidemAmbarlı22|
Pierangela Angelini23 | Iva Apostolova24| Mohammed A. S. Arfin Khan25,26 |
Elise Arnst27| Fabio Attorre20 | Christopher Baraloto28,29| Michael Beckmann30 |
Christian Berg31| Yves Bergeron32 | Erwin Bergmeier33 | Anne D. Bjorkman34,35|
Viktoria Bondareva36| Peter Borchardt37| Zoltán Botta-Dukát38 | Brad Boyle39|
Amy Breen40| Henry Brisse41| Chaeho Byun42 | Marcelo R. Cabido43|
Laura Casella23 | Luis Cayuela44 |TomášČerný45 | Victor Chepinoga46 |
János Csiky47 | Michael Curran48|RenataĆušterevska49|ZoraDajićStevanović19|
Els De Bie50 | Patrice de Ruffray51| Michele De Sanctis20 |
Panayotis Dimopoulos52| Stefan Dressler53| Rasmus Ejrnæs54| Mohamed Abd El-Rouf
Mousa El-Sheikh55,56| Brian Enquist39| Jörg Ewald57| Jaime Fagúndez58 |
Manfred Finckh59| Xavier Font60 | Estelle Forey61 | Georgios Fotiadis62|
Itziar García-Mijangos13| André Luis de Gasper63 | Valentin Golub36| Alvaro
G. Gutierrez64 | Mohamed Z. Hatim65| Tianhua He66 | Pedro Higuchi67 |
Dana Holubová7| Norbert Hölzel68 | Jürgen Homeier69| Adrian Indreica70|
DenizIşıkGürsoy71| Steven Jansen72 | John Janssen6| Birgit Jedrzejek68|
Martin Jiroušek7,7 3 | Norbert Jürgens59 |ZygmuntKącki74|AliKavgacı75 |
Elizabeth Kearsley76 | Michael Kessler77 | Ilona Knollová7| Vitaliy Kolomiychuk78|
Andrey Korolyuk79| Maria Kozhevnikova80|ŁukaszKozub81|DanielKrstonošić82|
Hjalmar Kühl2,83| Ingolf Kühn1,2,84 | Anna Kuzemko85|FilipKüzmič86|
Flavia Landucci7| Michael T. Lee87| Aurora Levesley88| Ching-Feng Li89 |
Hongyan Liu90| Gabriela Lopez-Gonzalez88| Tatiana Lysenko91,92|ArminMacanović93|
*Thes e authors shou ld be considere d joint first au thors.
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Journal of Vegetation Science BRUELHEID E Et aL.
Parastoo Mahdavi94| Peter Manning35 | Corrado Marcenò13 |
Vassiliy Martynenko95| Maurizio Mencuccini96 | Vanessa Minden97 | Jesper
Erenskjold Moeslund54 | Marco Moretti98 | Jonas V. Müller99|
Jérôme Munzinger100 | Ülo Niinemets101 | Marcin Nobis102| Jalil Noroozi103|
Arkadiusz Nowak104 | Viktor Onyshchenko85| Gerhard E. Overbeck8| Wim
A. Ozinga6| Anibal Pauchard105| Hristo Pedashenko106 | Josep Peñuelas107,108 |
Aaron Pérez-Haase109,110 | Tomáš Peterka7|PetrPetřík111 | Oliver L. Phillips88 |
Vadim Prokhorov80|ValerijusRašomavičius112| Rasmus Revermann59|
John Rodwell113| Eszter Ruprecht114|SolvitaRūsiņa115| Cyrus Samimi116 | Joop
H.J. Schaminée6| Ute Schmiedel59| Jozef Šibík117 | Urban Šilc86|
ŽeljkoŠkvorc82 | Anita Smyth118| Tenekwetche Sop2,83| Desislava Sopotlieva24|
Ben Sparrow118|ZvjezdanaStančić119| Jens-Christian Svenning34 |
Grzegorz Swacha74 | Zhiyao Tang90| Ioannis Tsiripidis120| Pavel Dan Turtureanu121|
EminUğurlu122 | Domas Uogintas112|MilanValachovič117| Kim André Vanselow123|
Yulia Vashenyak124| Kiril Vassilev24| Eduardo Vélez-Martin8|
Roberto Venanzoni125 | Alexander Christian Vibrans126| Cyrille Violle127|
Risto Virtanen2,128,129 | Henrik von Wehrden130| Viktoria Wagner131| Donald
A. Walker132| Desalegn Wana133| Evan Weiher134 | Karsten Wesche2,135,136|
Timothy Whitfeld137 | Wolfgang Willner103,138| Susan Wiser27 |
Thomas Wohlgemuth139| Sergey Yamalov140| Georg Zizka53| Andrei Zverev141
1Institute of Biology/Geobotany and Botanical Garden, Martin Luther University Halle-Wittenberg, Halle, Germany
2GermanCentreforIntegrativeBiodiversityResearch(iDiv)Halle-Jena-Leipzig,Leipzig,Germany
3VegetationEcologyGroup,InstituteofNaturalResourceSciences(IUNR),ZurichUniversit yofAppliedSciences(ZHAW),Wädenswil,Switzerland
4Plant Ecology, Bayreuth Center of Ecology and Environmental Research (BayCEER), University of Bayreuth, Bayreuth, Germany
5ResearchUnitofBiodiversity(CSUC/UO/PA),UniversityofOviedo,Mieres,Spain
6WageningenEnvironmentalResearch(Alterra),WageningenUniversityandResearch,Wageningen,TheNetherlands
7DepartmentofBotanyandZoology,Masar ykUniversity,Brno,CzechRepublic
8DepartmentofEcology,UniversidadeFederaldoRioGrandedoSul,PortoAlegre,Brazil
9FacultyofAgriculturalandEnvironmentalSciences,UniversityofRostock,Rostock,Germany
10MaxPlanckInstituteforBiogeochemistry,Jena,Germany
11Depar tmentofBiolog y,SantaClaraUniversit y,SantaClara,California
12GreatLakesForestryCentre,CanadianForestService,NaturalResourcesCanada,SaultSteMarie,Ontario,Canada
13PlantBiologyandEcology,UniversityoftheBasqueCountryUPV/EHU,Bilbao,Spain
14SchoolofGeography,UniversityofNottingham,Nottingham,UK
15EcologieetDynamiquesdesSystèmesAnthropisés(EDYSAN,UMR7058CNRS-UPJV),UniversitédePicardieJulesVerne,Amiens,France
16Depar tmentofBiology,UniversityofNorthCarolina,ChapelHill,NorthCarolina
17Depar tmentofCivilandEnvironmentalEngineering,UniversityoftheAndes,Bogota,Colombia
18DataandModellingCentre,SenckenbergBiodiversityandClimateResearchCentre(BiK-F),Frank furtamMain,Germany
19Depar tmentofAgrobotany,FacultyofAgriculture,Belgrade-Zemun,Serbia
20Depar tmentofEnvironmentalBiolog y,“Sapienza”UniversityofRome,Rome,Italy
21PlantNutrition,INRES,UniversityofBonn,Bonn,Germany
    
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BRUELHEID E Et aL.
22DepartmentofA griculturalBiotechnolog y,FacultyofAgricultureandNaturalSciences,DüzceUniversity,Düzce,Turkey
23BiodiversityConservationDepartment,ISPRA–ItalianNationalInstituteforEnvironmentalProtectionandResearch,Rome,Italy
24DepartmentofPlantandFungalDiversityandResources,InstituteofBiodiversityandEcosystemResearch,BulgarianAcademyofSciences,Sofia,Bulgaria
25Forestry&EnvironmentalScience,ShahjalalUniversityofScience&Technology,Sylhet,Bangladesh
26Disturbance Ecology, Bayreuth Center of Ecology and Environmental Research (BayCEER), Universit y of Bayreuth, Bayreuth, Germany
27ManaakiWhenua–LandcareResearch,Lincoln,NewZealand
28InternationalCenterforTropicalBotany(IC TB),TheKampongoftheNationalTropicalBotanicalGarden,CoconutGrove,Florida
29DepartmentofBiologicalSciences,FloridaInternationalUniversity,Miami,Florida
30LandscapeEcology,HelmholtzCentreforEnvironmentalResearch–UFZ,Leipzig,Germany
31BotanicalGarden,UniversityofGraz,Graz,Austria
32ForestResearchInstitute,UniversitéduQuébecenAbitibi-Témiscamingue,Rouyn-Noranda,Quebec,Canada
33Vegetation Ecology and Phytodiversity, University of Göttingen, Göttingen, Germany
34DepartmentofBioscience,CenterforBiodiversityDynamicsinaChangingWorld(BIOCHANGE)&SectionforEcoinformatics&Biodiversity,Aarhus
University,AarhusC,Denmark
35SenckenbergBiodiversityandClimateResearchCentre(SBiK-F),FrankfurtamMain,Germany
36LaboratoryofPhytocoenology,InstituteofEcologyoftheVolgaRiverBasin,Togliatti,RussianFederation
37InstituteofGeography,CEN–CenterforEarthSystemResearchandSustainability,Universit yofHamburg,Hamburg,Germany
38InstituteofEcologyandBotany,MTACentreforEcologicalResearch,Vácrátót,Hungary
39EcologyandEvolutionaryBiology,UniversityofArizona,Tucson,Arizona
40InternationalArcticResearchCenter,Universit yofAlaska,Fairbanks,Alaska
41FacultédesSciences,MEP,MarseilleCedex20,France
42SchoolofCivilandEnvironmentalEngineering,YonseiUniversity,Seoul,SouthKorea
43MultidisciplinaryInstituteforPlantBiology(IMBIV–CONICET),UniversityofCordoba–CONICET,Cordoba,Argentina
44DepartmentofBiology,Geology,PhysicsandInorganicChemistry,UniversidadReyJuanCarlos,Móstoles,Spain
45Depar tmentofForestEcology,FacultyofForestr yandWoodSciences,CzechUniversityofLifeSciencesPrague,Praha6–Suchdol,CzechRepublic
46LaboratoryofPhysicalGeographyandBiogeography,V.B.SochavaInstituteofGeographySBRAS,Irkutsk,RussianFederation
47Depar tmentofEcology,UniversityofPécs,Pécs,Hungary
48InstituteofEnvironmentalEngineering,SwissFederalInstituteofTechnology(ETH)Zürich,Zürich,Switzerland
49InstituteofBiology,FacultyofNaturalSciencesandMathematics,Skopje,RepublicofMacedonia
50TeamBiotopeDiversit y,ResearchInstituteforNatureandForest(INBO),Brussels,Belgium
51InstitutdeBiologieMoléculairedesPlantes(IBMP),UniversitédeStrasbourg,Strasburg,France
52Depar tmentofBiology,DivisionofPlantBiology,LaboratoryofBotany,UniversityofPatras,Patras,Greece
53DepartmentofBotanyandMolecularEvolution,SenckenbergResearchInstitute,FrankfurtamMain,Germany
54DepartmentofBioscience,AarhusUniversity,Roende,Denmark
55BotanyandMicrobiologyDepartment,CollegeofScience,KingSaudUniversity,Riyadh,SaudiArabia
56BotanyDepartment,FacultyofScience,DamanhourUniversity,Damanhour,Egypt
57HochschuleWeihenstephan-Triesdorf,UniversityofAppliedSciences,Freising,Germany
58FacultyofScience,UniversityofACoruña,ACoruña,Spain
59Biodiversity,EcologyandEvolutionofPlants,InstituteforPlantScience&Microbiology,Universit yofHamburg,Hamburg,Germany
60PlantBiodiversityResourceCentre,UniversityofBarcelona,Barcelona,Spain
61LaboratoireEcodiv,EA1293URAIRSTEA,NormandieUniversity,Mont-Saint-Aignan,France
62DepartmentofForestry&NaturalEnvironmentManagement,TEIofStereaEllada,Karpenissi,Greece
63DepartmentofNaturalScience,RegionalUniversityofBlumenau,Blumenau,Brazil
64DepartamentodeCienciasAmbientalesyRecursosNaturalesRenovables,FacultaddeCienciasAgronomicas,UniversidaddeChile,Santiago,Chile
65Botany,FacultyofScience,TantaUniversity,Tanta,Egypt
66SchoolofMolecularandLifeSciences,CurtinUniversity,Bentley,Australia
67ForestryDepartment,SantaCatarinaStateUniversity,Lages,Brazil
68InstituteofLandscapeEcolog y,UniversityofMünster,Münster,Germany
69Plant Ecology and Ecosystems Research, University of Göttingen, Göt tingen, Germany
70DepartmentofSilviculture,TransilvaniaUniversityofBrasov,Brasov,Romania
71DepartmentofBiology,CelalBayarUniversity,Manisa,Turkey
72InstituteofSystematicBotanyandEcology,FacultyofNaturalSciences,UlmUniversity,Ulm,Germany
73Depar tmentofPlantBiology,MendelUniversit yinBrno,Brno,CzechRepublic
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74BotanicalGarden,UniversityofWrocław,Wrocław,Poland
75SilvicultureandForestBotany,SouthwestAnatoliaForestResearchInstitute,Antalya,Turkey
76DepartmentofEnvironment,GhentUniversity,Gent,Belgium
77DepartmentofSystematicandEvolutionar yBotany,UniversityofZurich,Zurich,Switzerland
78O.V.FominBotanicalGardenattheEducationalandScientificCentre,InstituteofBiologyandMedicine,TarasShevchenkoNationalUniversityofKyiv,Kyiv,
Ukraine
79GeosystemLaboratory,CentralSiberianBotanicalGarden,SiberianBranch,RussianAcademyofSciences,Novosibirsk,RussianFederation
80InstituteofEnvironmentalSciences,KazanFederalUniversity,Kazan,RussianFederation
81DepartmentofPlantEcologyandEnvironmentalConser vation,FacultyofBiology,BiologicalandChemicalResearchCentre,UniversityofWarsaw,Warsaw,
Poland
82FacultyofForestry,UniversityofZagreb,Zagreb,Croatia
83Primatology,MaxPlanckInstituteforEvolutionaryAnthropology(MPI-EVA),Leipzig,Germany
84DepartmentofCommunityEcology,HelmholtzCentreforEnvironmentalResearch–UFZ,Halle,Germany
85M.G.KholodnyInstituteofBotany,NationalAcademyofSciencesofUkraine,Kyiv,Ukraine
86InstituteofBiology,ResearchCentreofSlovenianAcademyofSciencesandArts(ZRCSAZU),Ljubljana,Slovenia
87NatureServe,Durham,NorthCarolina
88SchoolofGeography,UniversityofLeeds,Leeds,UK
89SchoolofForestryandResourceConser vation,NationalTaiwanUniversity,Hsinchu,Taiwan
90CollegeofUrbanandEnvironmentalSciences,PekingUniversity,Beijing,China
91DepartmentofthePhytodiversityProblems,InstituteofEcologyoftheVolgaRiverBasinR AS,Togliatti,RussianFederation
92LaboratoryofVegetationScience,KomarovBotanicalInstituteRAS,Saint-Petersburg,Russia
93Depar tmentofBiolog y,CenterforEcologyandNaturalResources–AcademicianSulejmanRedžić,UniversityofSarajevo,Sarajevo,BosniaandHerzegovina
94ResearchGroupVegetationScience&NatureConser vation,DepartmentofEcologyandEnvironmentalScience,CarlvonOssietzky-UniversityOldenburg,
Oldenburg, Germany
95UfaInstituteofBiologyofUfaFederalScientificCentreoftheRussianAcademyofSciences,Ufa,RussianFederation
96CentreResearchEcolog yandForestryApplications(CREAF),ICREA,Barcelona,Spain
97InstituteofBiologyanEnvironmentalSciences,CarlvonOssietzky-UniversityOldenburg,Oldenburg,Germany
98BiodiversityandConservationBiology,SwissFederalResearchInstituteWSL,Birmensdorf,Swit zerland
99Conser vationScience,RoyalBotanicGardens,Kew,UK
100AMAP–BotanyandModellingofPlantArchitectureandVegetation,IRD,CIRAD,CNRS,INR A,UniversitéMontpellier,Montpellier,France
101CropScienceandPlantBiology,EstonianUniversityofLifeSciences,Tartu,Estonia
102InstituteofBotany,JagiellonianUniversity,Kraków,Poland
103DepartmentofBotanyandBiodiversityResearch,UniversityofVienna,Vienna,Austria
104BotanicalGarden–CenterforBiologicalDiversityConservation,PolishAcademyofSciences,Warszawa,Poland
105LaboratoriodeInvasionesBiológicas(LIB),UniversityofConcepción,Concepción,Chile
106Amsterdam,TheNetherlands
107GlobalEcologyUnitCRE AF-CSIC-UAB,CSIC ,Bellaterra,Spain
108CRE AF,CerdanyoladelVallès,Spain
109DepartmentofEvolutionaryBiology,EcologyandEnvironmentalSciences,UniversityofBarcelona,Barcelona,Spain
110ContinentalEcology,CenterforAdvancedStudiesofBlanes,SpanishResearchCouncil(CEAB-CSIC),Blanes,Girona,Spain
111Depar tmentofGISandRemoteSensing,InstituteofBotany,TheCzechAcademyofSciences,Průhonice,CzechRepublic
112InstituteofBotany,NatureResearchCentre,Vilnius,Lithuania
113Lancaster,UK
114HungarianDepartmentofBiologyandEcology,FacultyofBiologyandGeology,Babeș-BolyaiUniversity,Cluj-Napoca,Romania
115DepartmentofGeography,UniversityofLatvia,Riga,Latvia
116Climatology, Bayreuth Center of Ecology and Environmental Research (BayCEER), University of Bayreuth, Bayreuth, Germany
117InstituteofBotany,PlantScienceandBiodiversityCentre,SlovakAcademyofSciences,Bratislava,Slovakia
118TERN,UniversityofAdelaide,Adelaide,Australia
119FacultyofGeotechnicalEngineering,UniversityofZagreb,Varaždin,Croatia
120SchoolofBiology,AristotleUniversityofThessaloniki,Thessaloniki,Greece
121A.BorzaBotanicalGarden,Babeș-BolyaiUniversity,Cluj-Napoca,Romania
122ForestEngineeringDepartment,FacultyofForestry,BursaTechnicalUniversity,Yıldırım,Bursa,Turkey
123Depar tmentofGeography,UniversityofErlangen-Nuremberg,Erlangen,Germany
    
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 165
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BRUELHEID E Et aL.
124KhmelnytskyiInstituteofInterregionalAcademyofPersonnelManagement,Khmelny tsk yi,Ukraine
125DepartmentofChemistry,BiologyandBiotechnolog y,UniversityofPerugia,Perugia,Italy
126DepartamentodeEngenhariaFlorestal,UniversidadeRegionaldeBlumenau,Blumenau,Brazil
127Centred'EcologieFonctionnelleetEvolutive(UMR5175),CNRS–UniversitédeMontpellier–UniversitéPaul-ValéryMontpellier–EPHE,Montpellier,
France
128EcologyandGeneticsResearchUnit,BiodiversityUnit,UniversityofOulu,Oulu,Finland
129DepartmentofPhysiologicalDiversity,HelmholtzCenterforEnvironmentalResearch–UFZ,Leipzig,Germany
130InstituteofEcology,LeuphanaUniversity,Lüneburg,Germany
131DepartmentofBiologicalSciences,UniversityofAlberta,Edmonton,Canada
132InstituteofArcticBiology,UniversityofAlaska,Fairbanks,Alaska
133DepartmentofGeography&EnvironmentalStudies,AddisAbabaUniversity,AddisAbaba,Ethiopia
134DepartmentofBiology,UniversityofWisconsin–EauClaire,EauClaire,Wisconsin
135BotanyDepartment,SenckenbergMuseumofNaturalHistor yGörlitz,Görlit z,Germany
136InternationalInstituteZit tau,TechnicalUniversityDresden,Zittau,Germany
137Depar tmentofEcologyandEvolutionar yBiology/BrownUniversityHerbarium,BrownUniversity,Providence,RhodeIsland
138ViennaInstituteforNatureConservation&Analyses,Vienna,Austria
139ResearchUnitForestDynamics,SwissFederalInstituteforForest,SnowandLandscapeResearchWSL,Birmensdorf,Switzerland
140LaboratoryofWild-GrowingFlora,BotanicalGarden-Institute,UfaScientificCentre,RussianAcademyofSciences,Ufa,RussianFederation
141DepartmentofBotany,TomskStateUniversity,Tomsk,RussianFederation
Correspondence
Helge Bruelheide, Institute of Biology/
Geobotany and Botanical Garden, Martin
Luther University Halle-Wittenberg, Halle,
Germany.
Email:helge.bruelheide@botanik.uni-halle.de
Co-ordinatingEditor:AlessandroChiarucci
Abstract
Aims: Vegetat ion-plot records provide information on the pre sence and cover or
abundanceofplantsco-occurringinthe samecommunity.Vegetation-plotdataare
spread across research groups, environmental agencies and biodiversity research
centersand,thus,arerarelyaccessibleatcontinentalorglobalscales.Herewepre-
sentthesPlotdatabase,whichcollatesvegetationplotsworldwidetoallowforthe
explorationofglobalpatternsintaxonomic,functionalandphylogeneticdiversityat
theplantcommunitylevel.
Results: sPlotversion 2.1containsrecords from1,121,244vegetationplots,which
comprise23,586,216recordsofplantspeciesandtheirrelativecoverorabundance
inplotscollectedworldwidebetween1885and2015.Wecomplementedtheinfor-
mationforeachplotbyretrievingclimateandsoilconditionsandthebiogeographic
context (e.g., biomes) from external sources, and by calculating community- weighted
meansandvariancesoftraitsusinggap-filleddatafrom theglobalplant traitdata-
baseTRY.Moreover,wecreatedaphylogenetic treefor 50,167out ofthe 54,519
speciesidentifiedintheplots.Wepresentthefirstmapsofglobalpatternsofcom-
munityrichnessandcommunity-weightedmeansofkeytraits.
Conclusions: TheavailabilityofvegetationplotdatainsPlotoffersnewavenuesfor
vegetation analysis at the global scale.
KEYWORDS
biodiversity, community ecology, ecoinformatics, functional diversity, global scale,
macroecology,phylogeneticdiversity,plotdatabase,sPlot,taxonomicdiversity,vascular
plant,vegetationrelevé
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1 | INTRODUCTION
Studyingglobalbiodiversitypatternsisatthecoreofmacroecologi-
calresearch(Costello,Wilson,&Houlding,2012;Kreft&Jetz,2007;
Wiens, 2011),since theirexploration may provideinsightsintothe
ecological and evolutionary processes acting at different spatio-
temporalscales(Ricklefs,2004).The opportunitiesengenderedby
thecompilationof largecollectionsof biodiversitydataintowidely
accessibleglobal(GBIF,www.gbif.org)orcontinentaldatabases(e.g.,
BIEN,www.bien.nceas.ucsb.edu/bien)haverecentlyadvancedour
understandingofglobalbiodiversitypatterns, especiallyfor verte-
brates, b ut also for vascu lar plants (Bu tler etal., 2017; Engemann
etal., 2016; Lamannaetal., 2014;Swenson etal., 2012).Although
thisdevelopmenthasledtotheformulationofseveralmacroecolog-
icaltheories (Currieetal., 2004; rtel, Bennett,&Zobel, 2016), a
moremechanisticunderstandingofhowassemblyprocessesshape
ecological communities, andconsequently global biodiversity pat-
terns, is still missing (Lessard, Belmaker, Myers, Chase, &Rahbek,
2012).
Understandingthe links between biodiversity patternsand as-
semblyprocessesrequiresfine-graindataonthe co-occurrenceof
species in ecological communities, sampled across continental or
global spatial extents (Beck etal.,2012; Wiszetal., 2013).Forex-
ample,suchco-occurrencedatahavebeenusedtocomparechanges
in vegetation composition over time spans of decades (Jandt, von
Wehrden, & Bruelheide, 2011; Perring etal.,2018).Unfortunately,
upto now informationon fine-grain vegetationdata hasnot been
readily available, as most of the continental to global biodiversity
dataset s have been derive d from occurrence d ata (i.e., presen ce-
only data), and after being aggregated spatially, have a relatively
coarse- grain scale (e.g., one- degree grid cells) without information
onspeciesco-occurrenceatthemeaningfulscaleoflocalcommuni-
ties(Boakesetal.,2010).Incontrast,vegetation-plotdatarecordthe
coverorabundanceof eachplantspeciesthatoccursinaplotofa
givensizeatthedateofthesurvey,representingthemainreservoir
ofplantcommunitydataworldwide(Dengleretal.,2011).
Vegetation-plotdatadifferinfundamentalwaysfromdatabases
ofoccurrencerecords of individualspeciesaggregatedatthelevel
ofgridcellsorregionsofhundreds orthousands ofsquarekilome-
ters(Figure1).First,vegetationplotsusuallyprovideinformationon
therelativecoverorrelativeabundanceofspecies,allowingforthe
testingofcentraltheoriesofbiogeography,suchastheabundance–
rangesizerelationship(Gaston&Curnutt,1998)ortherelationship
between local abundance and niche breadth (Gaston et al., 2000).
Second,theycontaininformationonwhichplantspeciesco-occurin
thesamelocality(Chytrýetal.,2016),whichisanecessaryprecon-
ditionfordirect biotic interactionsamong plant individuals.Third,
unrecorded species can be considered truly absent from the abo-
veground vegetationat thisscalebecause thestandardizedmeth-
odologyoftakingavegetationrecord requiresasystematicsearch
forallspeciesinaplot,oratleastallspeciesofthedominantfunc-
tional gro up. Fourth, many p lots are spatiall y explicit and can b e
resur veyed through ti me to assess poss ible conseque nces of land
useandclimatechange(Perringetal.,2018;Steinbaueretal.,2018).
Fifth,vegetationplotsrepresentasnapshotoftheprimaryproduc-
ers of aterrestrialecosystem, which can befunctionally linked to
FIGURE1 Conceptualfigure
visualizinghowfunctionalcomposition
(inthiscaseplantheight)differsbetween
calculations based on mean traits for
gridcellsandcommunitydatasampledin
vegetationplots.Occurrencedata(e.g.,
fromdistributionatlases,GBIF,etc.)can
be used to calculate mean trait values in
grid cells G1–G3. However, community
weighted means (CWMs) of traits differ
acrosslocalplots(P1–P6),whilethemean
values of CWMs in the grid cells differ
from the unweighted values calculated in
thegridcells.Thisexampleissimplified
byshowingfewspeciesandfewplots.
In reality, differences are generally more
pronounced
    
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Journal of Vegetation Science
BRUELHEID E Et aL.
organismsfromdifferenttrophicgroupssampled inthesame plots
(e.g.,multiple-taxasurveys)andrelatedprocessesandservicesboth
below(e.g.,decomposition,nutrientcycling)andaboveground(e.g.,
herbivory,pollination)(e.g.,Schuldtetal.,2018).
Recently several projects at the regional to continental scale
havedemonstrated thepotentialofusing vegetation-plotdatabases
forexploringbiodiversitypatternsandtheunderlyingassemblypro-
cesses.UsingvegetationdataofFrenchgrasslands,Borgyetal.(2017)
demonstratedthatweightingleaftraitsbyspeciesabundanceinlocal
communitiesispivotaltocaptureleaftrait–environmentrelationships.
AnalyzingUnitedStatesforestassemblagessurveyedatthecommu-
nitylevel,Šímová,Rueda,andHawkins(2017)wereabletorelatecold
ordroughttolerancetoleaftraits,dispersaltraitsandtraitsrelatedto
stemhydraulics.Usingplot-basedtreeinventoriesoftheUnitedStates
forestservice,Zhang,Niinemets,Sheffield,andLichstein(2018)found
thatshiftsintreefunctionalcompositionamplifytheresponseoffor-
estbiomasstodroughts.Basedon>15.000 plotsfrom a widenum-
berofhabitattypesinDenmark,Moeslundetal.(2017)showedthat
typicalplantspeciesthatarepartofthesite-specificspeciespoolbut
areabsentinacommunitytendtodependonmycorrhiza,aremostly
adaptedtolowlightandlownutrientlevels,havepoordispersalabili-
ties and are ruderals and stress- intolerant. By collating >40,000 vege-
tationplotssampledinEuropeanbeechforests,Jiménez-Alfaroetal.
(2018)foundthatcurrentlocalcommunitydiversityandspeciespool
sizescalculatedatdifferentscalesweremainlyexplainedbyproximity
toglacialrefugiaandcurrentprecipitation.
Although large collections of vegetation-plot data are now
available from national to continental levels (e.g., Chytrý et al.,
2016;Enquist, Condit, Peet, Schildhauer,&Thiers,2016; Peet, Lee,
Jennings, & Faber-Langendoen, 2012; Schaminée, Hennekens,
Chytrý,& Rodwell, 2009; Schmidt etal.,2012), they arerarelyused
inglobal-scalebiodiversityresearch(Franklin,Serra-Diaz,Syphard,&
Regan, 2017; Wiser, 2016). This is unfortunate because vegetation-
plotdata may revealimportant patterns thatcannot be captured by
grid-based datasets (Table1). Functional composition patterns, for
instance, may differ substa ntially when considering vegetation-p lot
dataratherthansinglespeciesoccurrencesaggregatedatthelevelof
coarse-graingridcells.Usingplantheightasanillustrationrevealsthat
thetraitmea nscal culatedonallthespecie soccurringinagridcellmay
differ strongly from the community- weighted means (CWMs) aver-
agedacrosslocalcommunities(Figure1).Nevertheless,onlythegrid-
based approach hasbeenused to date in studies ofthe geographic
distribution of trait values (e.g., Swenson etal., 2012, 2017;Wright
et al., 2017).
Here,wepresentsPlot, a globaldatabaseforcompilingandin-
tegratingplantcommunitydata.Wedescribe(a)mainstepsininte-
gratingvegetation-plotdatainarepositorythatprovidestaxonomic,
functionalandphylogeneticinformationonco-occurringplantspe-
ciesandlinksittoglobalenvironmentaldrivers;(b)principalsources
andpropertiesofthedataandtheprocedurefordatausage;and(c)
expe cte dimpa ctsoftheda tab aseinfu turee cologicalresearch.Toil-
lustratethepotentialofsPlotwealsoshowglobaldiversitypatterns
that can be readily derived from the current content.
2 | COMPILATIONOFTHESPLOT
DATABASE
2.1 | Vegetation-plotdata
The sPlot co nsortium cur rently collate s 110 vegetatio n-plo td ata-
basesofregional,nationalorcontinental extent.Someofthedata-
baseshavepreviouslybeenaggregatedbyandcontributedthrough
TABLE1 Typesofinformationprovidedbysinglevegetationplots,vegetationplotsaggregatedwithingridcells(orothergeographic
units)andsinglespeciesoccurrencerecordsaggregatedwithingridcells.ThethreelevelsareillustratedinFigure1
Information from… Singlevegetationplots Setofvegetationplotsaggregatedwithin
grid cells
Grid- cell data from floristic
inventories
To derive information
on the …
Plot level Grid cell level Grid cell level
Typeofoccurrence Co- occurrence, occurrence by
vegetationtype
Occurrencebyvegetationtype Occurrence
Community assembly
rules
Yes(co-occurrenceisaprerequisite
forspeciesinteractions)
No No
Absences Yes(forthetargetplantgroupina
study)
No(exceptforintensivesamplingschemes) Dependingonsamplingintensity
Floristiccomposition … of the local community …ofthespeciespoolsofvegetationtypes …ofthetotalsetofspecies
Diversity α, γγ
Speciesabundance Local cover- abundance Mean cover- abundance and frequency by
vegetationtype
Occurrence only
Combination with
traits
Functionalcompositionofthelocal
community (traits unweighted or
weighted by cover: CWM, CWV)
Functionalcompositionofthespeciespool
(unweighted or weighted)
Functionalcompositionofthetotal
setofspecies(unweightedonly)
Environmental
filtering
… at the local level … at the regional level … at the regional level
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two (sub-)continental database initiatives (Table2 and Appendix
S1).All datafrom Europeand nearby regionswere contributed via
theEuropeanVegetationArchive (EVA), usingtheSynBioSystaxon
databas e as a standard t axonomic bac kbone (Chytr ý etal., 2016).
Three African databaseswere contributedvia theTropical African
VegetationArchive(TAVA).Inaddition,multipleU.S.databaseswere
contributedthroughtheVegBankarchive maintained in support of
theU.S. National Vegetation Classification (Peet,Lee, Boyle, etal.,
2012; Peet, L ee, Jennings, & Fab er- Lange ndoen, 2012). The dat a
fromotherregions(SouthAmerica,Asia)werecontributedassepa-
rate databases.
Westoredthevegetation-plotdatafromtheindividualdatabases
inthedatabasesoftwareTURBOVEGv2(Hennekens&Schaminée,
2001).Ourgeneralprocedurewastopreservetheoriginalstructure
andcontent ofthedatabases as much as possibleinorder to facil-
itateregularupdatesthroughautomatedworkflows.Theindividual
databaseswerethenintegratedintoasingleSQLitedatabaseusing
TURBOVEGv3(S.M.Hennekens,ALTERRA,TheNetherlands;www.
synbiosys.alterra.nl/turboveg3/help/en/index.html).TURBOVEGv3
combinesthespecieslistsfromtheoriginaldatabasesinasinglere-
positoryand links the plotattributes(so-calledheaderdata) to 58
descriptors of vegetation-plots (Table S2.1 in Appendix S2). The
metadata of the databases collated in sPlot were managed through
the Global Index of Vegetation- Plot Databases (GIVD; Dengler et al.,
2011), using the GIVD ID as the identifier. The current sPlot version
2.1 was created in October 2016 and contains 1,121,244 vegetation
plotswith23,586,216plantspecies×plotobservations(i.e.,records
ofa species in a plot).Mostrecords(1,073,737;95.8%)haveinfor-
mationoncover,29,288onpresence/absence,5,854onbasalarea,
4,883 on number of stems (often in addition to basal area), 148 on
importancevalue(acombinationofbasalareaandnumberofstems),
3,265oncountsofindividuals,1,895onpercentagefrequency,and
further2,174haveamixofthesedifferenttypesofmetrics.
2.2 | Taxonomicstandardization
TocombinethespecieslistsofthedifferentdatabasesinsPlot,we
constructedataxonomicbackbone.Tolinkco-occurrenceinforma-
tion in sPlotwith plant traits, weexpandedthis backbonetointe-
grateplantnamesusedintheTRYdatabase(Kattgeetal.,2011).The
taxon names (without nomenclatural authors) from sPlot 2.1 and
TRY3.0were first concatenatedintoone list,resulting in 121,861
names, of which 61,588 (50.5%) were unique to sPlot; 35,429
(29.1%)uniquetoTRY;and24,844(20.4%)sharedbetweenTRYand
sPlot.Taxonnameswereparsedandresolvedusing the Taxonomic
NameResolutionServicewebapplication(TNRSversion4.0;Boyle
etal.,2013;iPlantCollaborative, 2015),using thefive TNRSstand-
ardsourcesranked bydefault.Weallowed for(a)partialmatching
tothenexthigherrank(genusorfamily)ifthefulltaxonnamecould
notbefoundand(b)fullfuzzymatching,toreturnnamesthatwere
matched within a maximum number of four single- character edits
(Levenshteineditdistanceof4),whichcorrespondstotheminimum
matchaccuracyof0.05inTNRS,with1indicatingaperfectmatch.
Weaccepted all names thatwere matched,or converted from
synonyms, with an overall match score of 1. In cases with no exact
match(i.e., the overallmatchscore was <1),names were inspected
onan individual basis. Allnames thatmatchedattaxonomic ranks
atorlower thanspecies(e.g., subspecies,varieties)were accepted
ascorrectnames. Thename matchingprocedurewas repeatedfor
the uncertain names (i.e., with match accuracy scores below the
thresholdvaluefrom thefirstmatchingrun), withapreference on
first usingthesource‘Tropicos’(MissouriBotanicalGarden;http://
www.tropicos.org/;accessed 19Dec2014)becausehere matching
scoreswereoftenhigherfornamesoflowtaxonomicrank.There-
maining 9,641 non- matched names were resolved using (a) the addi-
tionalsource‘NCBI’(Federhen,2010)withinTNRS,(b)thematching
toolsinthePlant Listwebapplication(ThePlantList2013),(c)the
‘tpl’-functionwithin the R-package Taxonstand’(Cayuela, Stein, &
Oksanen,2017)and(d)manualinspection(i.e.,toresolvevernacular
names).Allsubspecieswereaggregatedtothespecieslevel.Names
thatcould notbematchedwereclassified as ‘Nosuitablematches
found’.BecausesPlotandTRYcontaintaxaofnon-vascularplants,
wetaggedvascularplant names basedontheirfamily andphylum
affiliation, usingthe‘rgbif’library in R (Chamberlain,2017).Of the
fulllistofplantnamesinsPlotandTRY,79,171(94.6%)plantnames
werematched atthespecieslevel,4,343(5.2%)atthegenus level,
152(0.2%)atthefamilyleveland13namesathighertaxonomiclev-
els.Overall,thisledto58,066acceptedtaxonnamesinsPlot.Family
affiliationwasclassifiedaccordingto APG III (APGIII,2009).Ade-
tailed descriptionofthe workflow, including R-code, is available in
Purschke(2017a).
One potent ial shortco ming of our taxon omic backbon e is that
formostregionsitwasnecessarytostandardizetaxausingstandard
setsoftaxonomicsynonyms.Thus,ifataxonomicnamerepresents
multipletaxonomic concepts,e.g., such as created by the splitting
andlumpingoftaxa,oranamehasbeenmisappliedinaregion,we
musttrustthatthisproblemhasbeenaddressedinourcomponent
databases(Franz,Peet,&Weakley,2004;Jansen&Dengler,2010).
However,differentcomponentdatabasesmayhaveapplieddiffer-
enttaxonomicconceptsforsplittingandlumpingtaxa.
2.3 | Physiognomicinformation
Toachieveaclassificationintoforestsversusnon-foreststhatisap-
plicable to all plots irrespective of the structural andhabitat data
provided by the source database, we defined as forest all plot re-
cordsthathad>25%absolutecoverofthetreelayer,makinguseof
the attribute data of sPlot. This threshold is similar to the classifica-
tionofEllenbergandMüller-Dombois(1967),whodefinedwoodland
formationswithtreescoveringmorethan30%.Therewere16,244
tree species inthe sPlot database.As tree layercover wasavaila-
ble for only 25% of allplots, we additionally usedthe information
whethe r the taxa pre sent in a plot were tr ees (usually def ined as
beingtallerthan5m),usingtheplantgrowthforminformationfrom
TRY (see bel ow). Thus, plots lac king tree cover infor mation were
defined as forests if the sum of relative cover of all tree taxa was
    
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 169
Journal of Vegetation Science
BRUELHEID E Et aL.
TABLE2  Plot datasets included in sPlot 2.1
GIVD ID Database name
# of plots in
sPlot 2 .1 Custodian Deputy custodian Reference
[Aggregator] European Vegetation Archive
(EVA)
950,001 Milan Chytrý Ilona Knollová Chytrý et al. (2016)
00- 00- 004 Vegetation Database of Eurasian
Tundra
1,132 Risto Virtanen
00- RU- 001 VegetationDatabaseForestof
SouthernUral
1,102 VassiliyMartynenko
00- RU- 003 DatabaseMeadowsandSteppes
ofSouthernUral
2,354 SergeyYamalov Mariya Lebedeva
00- TR- 001 ForestVegetationDatabaseof
Turkey-FVDT
919 AliKavgacı
00- TR- 002* Non-forestVegetationDatabase
ofTurkey
3,018 DenizIşıkGürsoy DidemAmbarlı
A S - T R - 0 0 2 VegetationDatabaseofOak
CommunitiesinTurkey
1,181 EminUğurlu
EU- 00- 002 Nordic-BalticGrassland
VegetationDatabase(NBGVD)
7,6 75 JürgenDengler ŁukaszKozub DenglerandRūsiņa
(2012)
EU- 00- 011 Vegetation- Plot Database of the
University of the Basque
Country (BIOVEG)
18, 441 Idoia Biurrun ItziarGarcía-Mijangos Biurrun, García-
Mijangos,Campos,
Herrera, and Loidi
(2012)
EU- 00- 013 BalkanDryGrasslandsDatabase 7,683 KirilVassilev ArminMacanović Vassilev,Dajič,
Ćušterevska,
Bergmeier, and
Apostolova(2012)
EU- 00- 016 MediterraneanAmmophiletea
Database
7,35 9 Corrado Marcenò BorjaJiménez-Alfaro Marcenò and
Jiménez-Alfaro
(2017 )
EU- 00- 017 EuropeanCoastalVegetation
Database
4,624 John Janssen
EU- 00- 018 TheNordicVegetationDatabase 5,477 Jonathan Lenoir Jens- Christian
Svenning
Lenoir et al. (2013)
EU- 00- 019 BalkanVegetationDatabase 9,118 KirilVassilev HristoPedashenko Vassilev et al. (2016)
EU- 00- 020 WetVegEurope 14,111 FlaviaLanducci Landucci et al. (2015)
EU- 00- 022 EuropeanMireVegetation
Database
10,147 TomášPeterka MartinJiroušek Peterka,Jiroušek,
Hájek,and
Jiménez-Alfaro
(2015)
E U - A L - 0 0 1 VegetationDatabaseofAlbania 290 MicheleDeSanctis GiulianoFanelli DeSanctis,Fanelli,
Mullaj,andAttorre
(2017 )
E U - A T - 0 0 1 AustrianVegetationDatabase 34,458 Wolfgang Willner Christian Berg Willner, Berg, and
Heiselmayer (2012)
EU- BE- 002 INBOVEG 25,665 Els De Bie
EU- BG- 001 Bulgarian Vegetation Database 5,254 IvaApostolova DesislavaSopotlieva Apostolova,
Sopotlieva,
Pedashenko,Velev,
and Vasilev (2012)
EU- CH- 005 SwissForestVegetation
Database
14,193 Thomas Wohlgemuth Wohlgemuth (2012)
E U - C Z - 0 0 1 CzechNationalPhytosociological
Database
104,697 Milan Chytr ý DanaHolubová ChytrýandRafajová
(2003)
(Continues)
170 
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Journal of Vegetation Science BRUELHEID E Et aL.
GIVD ID Database name
# of plots in
sPlot 2 .1 Custodian Deputy custodian Reference
EU- DE- 001 VegMV 53,822 FlorianJansen Christian Berg Jansen, Dengler, and
Berg (2012)
EU- DE- 013 VegetWeb Germany 23,078 Jörg Ewald Ewald, May, and
Kleikamp(2012)
EU- DE- 014 German Vegetation Reference
Database (GVRD)
30,840 Ute Jandt Helge Bruelheide Jandt and Bruelheide
(2012)
E U - D K - 0 0 2 NationalVegetationDatabaseof
Denmark
24,264 JesperErenskjold
Moeslund
Rasmus Ejrnæs
E U - E S - 0 0 1 Iberian and Macaronesian
VegetationInformationSystem
(SIVIM)̶Wetlands
6,560 AaronPérez-Haase XavierFont
E U - F R - 0 0 3 SOPHY 2 0 9,864 Henry Brisse Patrice de Ruffray Brisse, de Ruffray,
Grandjouan, and
Hof f (1995)
EU- GB- 001 UKNationalVegetation
Classification Database
28,533 JohnS.Rodwell
EU- GR- 001 KRITI 292 Erwin Bergmeier
EU- GR- 005 HellenicNatura2000Vegetation
Database(HelNatVeg)
5,168 PanayotisDimopoulos IoannisTsiripidis Dimopoulosand
Tsiripidis(2012)
EU- GR- 006 Hellenic Woodland Database 3,199 GeorgiosFotiadis IoannisTsiripidis Fotiadis,Tsiripidis,
Bergmeier, and
Dimopoulos(2012)
EU- HR- 0 01 Phytosociological Database of
Non-ForestVegetationin
Croatia
5,057 ZvjezdanaStančić Stančić(2012)
EU- HR- 002 Croatian Vegetation Database 8,734 ŽeljkoŠkvorc DanielKrstonošić
EU- HU- 003 CoenoDat Hungarian
Phytosociological Database
8,505 JánosCsiky ZoltánBotta-Dukát Lájeretal.(2008)
EU- IT- 001 VegItaly 15,332 RobertoVenanzoni FlaviaLanducci Landucci et al. (2012)
EU- IT- 010 ItalianNationalVegetation
Database(BVN/ISPRA)
3,562 Laura Casella PierangelaAngelini Casella, Bianco,
Angelini,and
Morroni (2012)
EU- IT- 011 Vegetation- Plot Database
SapienzaUniversityofRome
(VPD-Sapienza)
12,780 EmilianoAgrillo FabioAttorre Agrilloetal.(2017)
EU - LT- 0 01 Lithuanian Vegetation Database 7,82 1 ValerijusRašomavičius Domas Uogintas
EU - LV- 0 01 Semi-naturalGrassland
Vegetation Database of Latvia
5,594 SolvitaRūsiņa Rūsiņa(2012)
E U - M K - 0 0 1 Vegetation Database of the
RepublicofMacedonia
1,417 RenataĆušterevska
E U - N L - 0 0 1 DutchNationalVegetation
Database
102,327 JoopH.J.Schaminée StephanM.
Hennekens
Schaminéeetal.
(2006)
EU - P L- 0 0 1 Polish Vegetation Database 22,229 ZygmuntKącki GrzegorzSwacha KąckiandŚliwiński
(2012)
EU- RO- 007 RomanianForestDatabase 6,017 AdrianIndreica Pavel Dan Turtureanu Indreica, Turtureanu,
Szabó,andIrimia
(2017 )
EU- RO- 008 Romanian Grassland Database 1,921 EszterRuprecht KirilVassilev Vassilev et al. (2018)
E U - R S - 0 0 2 Vegetation Database Grassland
VegetationofSerbia
5,587 SvetlanaAćić ZoraDajićStevanović Aćić,Petrović,Šilc,
andDajić
Stevanović(2012)
TABLE2 (Continued)
(Continues)
    
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 171
Journal of Vegetation Science
BRUELHEID E Et aL.
GIVD ID Database name
# of plots in
sPlot 2 .1 Custodian Deputy custodian Reference
EU- RU- 002 Lower Volga Valley
Phytosociological Database
14,853 Valentin Golub ViktoriaBondareva Golub et al. (2012)
EU- RU- 003 Vegetation Database of the
Volga and the Ural Rivers
Basins
1, 516 TatianaLysenko Lysenko,
Mitroshenkova,and
Kalmykova(2012)
EU- RU- 011 Vegetation Database of
Tatarstan
7,4 71 VadimProkhorov MariaKozhevnikova Prokhorov,Rogova,
andKozhevnikova
(2017 )
E U - S I - 0 0 1 VegetationDatabaseofSlovenia 10,986 Urban Šilc FilipKüzmič Šilc (2012)
E U - S K - 0 0 1 SlovakVegetationDatabase 36,405 MilanValachovič JozefŠibík Šibík(2012)
E U - U A - 0 0 1 UkrainianGrasslandsDatabase 4,043 AnnaKuzemko YuliaVashenyak Kuzemko(2012)
E U - U A - 0 0 6 VegetationDatabaseofUkraine
andAdjacentPartsofRussia
3,326 ViktorOnyshchenko VitaliyKolomiychuk
[Aggregator] Tropical African Vegetation
Archi ve (TAVA)
6,677 Marco Schmidt Stefan Dressler Janßen et al. (2011)
AF-00-001 WestAfricanVegetation
Database
3,129 MarcoSchmidt GeorgZizka Schmidtetal.(2012)
AF-00-008 PANAFVegetationDatabase 2,469 HjalmarKühl TeneKwetcheSop
A F - B F - 0 0 1 SahelVegetationDatabase 1,079 JonasV.Müller MarcoSchmidt Müller(2003)
Other databases 164,566
00- 00- 001 RAINFORdatamanagedby
ForestPlots.net
1,827 OliverL.Phillips AuroraLevesley Lopez-Gonzalez,
Lewis,Burkitt,and
Phillips(2011)
00- 00- 003 SALVIAS 4,883 Brian Enquist Brad Boyle
00- 00- 005 Tundra Vegetation Plots
(TundraPlot)
577 AnneD.Bjorkman SarahElmendorf Elmendorf et al.
(2012)
00- RU- 002 DatabaseofMasaryk
University’sVegetation
ResearchinSiberia
1,5 47 Milan Chytr ý Chytrý (2012)
AF-00-003 BIOTASouthernAfrica
Biodiversity Observatories
Vegetation Database
1,666 NorbertJürgens Gerhard Muche Muche,Schmiedel,
andJürgens(2012)
AF-00-006 SWEA-Dataveg 2,704 MiguelAlvarez Michael Curran
AF-00-009 Vegetation Database of the
OkavangoBasin
590 Rasmus Revermann ManfredFinckh Revermann et al.
(2016)
A F - C D - 0 0 1 ForestDatabaseofCentral
Congo Basin
292 ElizabethKearsley HansVerbeeck Kearsleyetal.(2013)
A F - E T - 0 0 1 VegetationDatabaseofEthiopia 74 Desalegn Wana AnkeJentsch Wana and
Beierkuhnlein
(2011)
A F - M A - 0 0 1 Vegetation Database of
SouthernMorocco
1,337 ManfredFinckh Finckh(2012)
A F - Z A - 0 0 3 * SynBioSysFynbosVegetation
Database
3,810 John Janssen
A F - Z W - 0 0 1 * Vegetation Database of
Zimbabwe
36 CyrusSamimi Samimi(2003)
AS-00-001 KoreanForestDatabase 4,885 TomášČerný PetrPetřík Černýetal.(2015)
AS-00-003 VegetationofMiddleAsia 1,381 ArkadiuszNowak MarcinNobis Nowaketal.(2017)
AS-00-004 RiceFieldVegetationDatabase 179 ArkadiuszNowak
TABLE2 (Continued)
(Continues)
172 
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Journal of Vegetation Science BRUELHEID E Et aL.
GIVD ID Database name
# of plots in
sPlot 2 .1 Custodian Deputy custodian Reference
A S - B D - 0 0 1 TropicalForestDatasetof
Bangladesh
211 MohammedA.S.Arfin
Khan
FahmidaSultana
A S - C N - 0 0 1 ChinaForest-SteppeEcotone
Database
148 Hongyan Liu FengjunZhao Liu, Cui, Pott, and
Speier(2000)
A S - C N - 0 0 2 Tibet-PaDeMoSGrazing
Transect
146 KarstenWesche Wang et al. (2017)
A S - C N - 0 0 3 * VegetationDatabaseoftheBEF
China Project
27 Helge Bruelheide Bruelheide et al.
(2011)
A S - C N - 0 0 4 * Vegetation Database of the
NorthernMountainsinChina
485 ZhiyaoTang
A S - C N - 0 0 5 * DatabaseSteppeVegetationof
Xinjiang
129 KoheiSuzuki
A S - E G - 0 0 1 VegetationDatabaseofSinaiin
Egypt
926 MohamedZ.Hatim Hatim (2012)
A S - I D - 0 0 1 SulawesiVegetationDatabase 24 MichaelKessler
A S - I R - 0 0 1 Vegetation Database of Iran 2,335 JalilNoroozi Parastoo Mahdavi
A S - K G - 0 0 1 Vegetation Database of
South-WesternKyrgyzstan
452 Peter Borchardt UdoSchickhoff Borchardt and
Schickhoff(2012)
A S - K Z - 0 0 1 Database of Meadow Vegetation
intheNWTianShanMountains
94 ViktoriaWagner Wagner (2009)
A S - M N - 0 0 1 SouthernGobiProtectedAreas
Database
1, 516 HenrikvonWehrden KarstenWesche von Wehrden,
Wesche, and Miehe
(2009)
A S - R U - 0 0 1 Wetland Vegetation Database of
BaikalSiberia(WETBS)
2,381 VictorChepinoga Chepinoga(2012)
A S - R U - 0 0 2 DatabaseofSiberianVegetation
(DSV)
9,116 AndreyKorolyuk AndreiZverev
A S - R U - 0 0 4 Database of the University of
Münster-Biodiversityand
EcosystemResearchGroup’s
Vegetation Research in
WesternSiberiaand
Kazakhstan
445 NorbertHölzel Wanja Mathar
A S - S A - 0 0 1 * VegetationDatabaseofSaudi
Arabia
919 MohamedAbd
El- Rouf Mousa
El-Sheikh
A S - T J - 0 0 1 Eastern Pamirs 282 KimAndréVanselow Vanselow (2016)
A S - T W - 0 0 1 NationalVegetationDatabaseof
Taiwan
930 Ching-FengLi Chang-FuHsieh
A S - Y E - 0 0 1 SocotraVegetationDatabase 396 MicheleDeSanctis FabioAttorre DeSanctisand
Attorre(2012)
A U - A U - 0 0 2 TERNAEKOS 21, 261 AnitaSmyth BenSparrow Turner,Smyth,
Walker,andLowe
(2017 )
A U - N C - 0 0 1 NewCaledonianPlantInventory
andPermanentPlotNetwork
(NC-PIPPN)
201 JérômeMunzinger PhilippeBirnbaum Ibanezetal.(2014)
A U - N Z - 0 0 1 NewZealandNational
VegetationDatabank
1,895 SusanWiser Wiser, Bellingham,
and Burrows (2001)
A U - P G - 0 0 1 ForestPlotsfromPapuaNew
Guinea
63 Timothy Whitfeld George Weiblen Whitfeld et al. (2014)
TABLE2 (Continued)
(Continues)
    
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BRUELHEID E Et aL.
>25%.Similarly,wedefinednon-forestsbycalculatingthecoverof
alltaxathatwerenotdefinedastreesorshrubs(alsotakenfromthe
TRYplantgrowth forminformation) and thatwere not taller than
2m,usingtheTRYdataonmeanplantheight.Intotal,21,888taxa
belongedtothiscategory.Wedefinedallplotsasnon-forestsifthe
sum of relative cover of these low- stature, non- tree and non- shrub
taxawas>90%.Aswedidnothavethegrowthformandheightin-
formationforalltaxa,afractionofabout25%oftheplotsremained
unassigned (i.e., neither forest, nor non- forest). In addition, more
detailedclassificationsofplotsintophysiognomicformations(Table
S3.2 inAppendix S3) and naturalness (TableS3.3in AppendixS3)
werederivedfrom varioustypesofplot-levelor database-level in-
formationprovidedbythesourcesandstoredinfiveseparatefields
(seeTableS2.1inAppendixS2).
2.4 | Phylogeneticinformation
We developed a wo rkflow to gene rate a phylogeny of t he vascu-
larplantspeciesinsPlot,usingthephylogenyofZanneetal.(2014),
updated by QianandJin (2016).SpeciespresentinsPlot but miss-
ing from th is phylogeny were ad ded next to a ran domly selec ted
congener (see also Mai tner etal., 2018). This approach has been
demonstrated to introduce less bias into subsequent analyses than
adding missing species as polytomies to the respective genera
(Davies,Kraft,Salamin,&Wolkovich,2012).Weonlyaddedspecies
based on taxonomic informationonthegenus level,thus not mak-
ing use of family affiliation. Because of the absence of congeners in
thereference phylogeny,7,147species couldnot be added (11.7%
ofallresolvedtaxa in sPlotand TRY).This resulted in a phylogeny
GIVD ID Database name
# of plots in
sPlot 2 .1 Custodian Deputy custodian Reference
NA-00-002 TreeBiodiversityNetwork
(BIOTREE-NET)
1,757 Luis Cayuela Cayuela et al. (2012)
N A - C A - 0 0 3 Database of Timberline
VegetationinNWNorth
America
110 ViktoriaWagner TobySpribille Wagner,Spribille,
Abrahamczyk,and
Bergmeier (2014)
N A - C A - 0 0 4 UnderstoryofSugarMaple
DominatedStandsinQuebec
and Ontario (Canada)
156 IsabelleAubin Aubin,Gachet,
Messier, and
Bouchard (2007)
N A - C A - 0 0 5 * BorealForestofCanada 89 YvesBergeron LouisDeGrandpré
N A - G L - 0 0 1 Vegetation Database of
Greenland
664 BirgitJedrzejek FredJ.A.Daniëls Sieg,Drees,and
Daniëls(2006)
N A - U S - 0 0 2 VegBank 67, 352 RobertK.Peet Michael T. Lee Peet et al. (2012)
N A - U S - 0 0 6 CarolinaVegetationSur vey
Database
17, 2 21 Rober tK.Peet Michael T. Lee Peet et al. (2012)
N A - U S - 0 1 4 Alaska-ArcticVegetationArchive 1,363 DonaldA.Walker AmyBreen Walkeretal.(2016)
SA-00-002 VegPáramo 2,643 Gwendolyn Peyre XavierFont Peyre et al. (2015)
S A - A R - 0 0 2 Vegetation Database of Central
Argentina
218 Marcelo R. Cabido AliciaAcosta
S A - B O - 0 0 3 BoliviaForestPlots 75 MichaelKessler SebastianHerzog
S A - B R - 0 0 2 ForestInventory,StateofSanta
Catarina,Brazil(IFFSCProject)
1,669 AlexanderChristian
Vibrans
AndréLuisdeGasper Vibrans,Sevegnani,
Lingner,deGasper,
andSabbagh(2010)
S A - B R - 0 0 3 GrasslandsofRioGrandedoSul,
Brazil
320 EduardoVélez-Martin ValérioDePattaPillar
S A - B R - 0 0 4 GrasslandDatabaseofCampos
Sulinos
161 GerhardE.Overbeck ValérioDePattaPillar
S A - C L - 0 0 2 SSAForests_Plots_db 261 AlvaroG.Gutierrez
S A - C L - 0 0 3 * ChileanParkTransects
-Fondecyt1040528
165 AníbalPauchard AliciaMarticorena Pauchard,Fuentes,
Jiménez,
Bustamante, and
Marticorena (2013)
S A - E C - 0 0 1 EcuadorForestPlotDatabase 172 JürgenHomeier
Note.GIVD IDrefersto theID inthe GlobalIndex ofVegetation-PlotDatabases(http://www.givd.info),whichmanagesthemetadataforsPlotand
providesupdatedonlinedescriptionsofthesedatabases;*aftertheGIVDIDindicatesthattherespectivedatabasedescriptioniscurrentlynotvisible
ontheGIVDwebsite.Datasetscontributedinharmonizedformatfromacontinentaldataaggregator(“collectivedatabase”accordingtothesPlotRules)
arelistedunderitsname.Furtherreferences,attributionsanddisclaimersforparticulardatasetsarefoundAppendixS1.
TABLE2 (Continued)
174 
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Journal of Vegetation Science BRUELHEID E Et aL.
with 54,067 resolved taxon names from61,214standardizedtaxa
inthe combined listofsPlot andTRY.Thetree wasfinallypruned
tothe vascularplanttaxaofthecurrentsPlotversion2.1,resulting
inaphylogenetictreefor53,489outofthe58,066taxainsPlot.Of
these 53,489 names, 16,026 are also found among the 31,389 taxa
inthephylogenetictreeofQianandJin(2016),i.e.,51.1%.Thefull
procedureandtheRcodeareavailableinPurschke(2017b).
2.5 | Associatedenvironmentalplotinformation
To complement the plot data , we harmonized geogr aphical coor-
dinates (in d ecimal degre es), elevation (m above s ea level), aspec t
(degrees)andslope(degrees)asprovided bythecontributing data-
bases. Allother variableswere too sparselyandtoo inconsistently
sampledacrossdatabasestobecombinedintheglobalset,butwere
retainedin theoriginal data sources and can beretrieved for par-
ticularpurposes.
Weusedthegeographiccoordinatestocreateageodatabasein
ArcGIS14.1(ESRI, Redlands, CA)to link sPlot2.1to theseclimate
and soil data. We retrieved data for all the 19 bioclimatic variables
providedbyCHELSAv1.1(Kargeretal.,2017)byaveragingclimatic
data fromtheperiod1979–2013at30 arc seconds (about1km in
grid cells near to the equator). These variables are the same as the
ones used in WorldClim (www.worldclim.org; Hijmans, Cameron,
Parra, Jones, & Jarvis, 2005), but calculated with a downscaling
approachbasedon estimates of the ERA-Interim climatic reanaly-
sis(Deeetal.,2011).WhiletheCHELSAclimatological datahave a
similar accuracyasother products for temperature, theyaremore
precise for precipitation patterns (Karger etal., 2017). We also
calculated growing degree days for 1°C (GDD1) and 5°C (GDD5),
accordingtoSynesandOsborne(2011)andbasedonCHELSAdata,
and included the index of aridityand potential evapotranspiration
extractedfromtheCGIAR-CSIwebsite(www.cgiar-csi.org).Inaddi-
tion,weextractedsevensoilvariablesfromtheSOILGRIDSproject
(https://soilgrids.org/; licensed by ISRIC – World SoilInformation),
downloaded at 250- m resolution and then converted to the same
30-arc second gridformat ofCHELSA. Toexplorethe distribution
ofsPlotdataintheglobalenvironmentalspace,wesubjectedall30
climateandsoilvariablesoftheglobalterrestrialsurfacerasterized
ona2.5arc-minutegridresolutiontoaprincipalcomponentanalysis
(PCA)onstandardizedandcentereddata.Wesubsequentlycreated
a grid of 100 cells×100 cells within the bi-dimensional environ-
mentalspacedefinedbythefirsttwoPCAaxes(PC1andPC2)and
countedthenumberofterrestrialcellsperenvironmentalgridcellof
thePC1–PC2space.Then,wecountedthenumberofplotsinsPlot
inthesamePCAgrid(Figure2).
Welinked all vegetation plots totwo global biome classifica-
tions. We use d the World Wildli fe Fund (WWF ) spatial info rma-
tion on ter restrial eco regions (Olson eta l., 2001) to assign p lots
tooneofthe867ecoregions,14biomesandeightbiogeographic
realms. TheWWFapproachisbased onabottom-upexpertsys-
tem using various regional biodiversity sources to define ecore-
gions, wh ich in turn are grou ped into realms an d biomes (Olson
etal., 2001).Inaddition, we created a shapefile fortheecozones
def inedbySchultz(2005)torepresentmajorbiome sinresponseto
globalclimaticvariation.Sincethesezonesareclimaticallyhetero-
geneousinmountainregions,wedifferentiatedanadditional“al-
pine”biomeformountainareasabovethelowermountainthermal
FIGURE2 DistributionofvegetationplotsfromsPlot2.1intheglobalenvironmentalspace.Comparisonofthedistributionofall
terrestrial2.5arc-minutecells(a)andplotsinsPlot2.1(b)intheprincipalcomponentanalysis(PCA)spacedefinedon30environmental
(climateandsoil)variables.ThePCAspacewasdividedintoa100×100regulargrid.Foreachelementofthisgrid,thegraphsshowthe
numberof2.5arc-minutecells(a)andplots(b),respectively.Colorsrefertothelogarithmofnumberofplots,withthelegendshowing
untransformednumberofplots.ThefirstandsecondPCAaxisexplained48.6%and27.3%ofthetotalvariance
    
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belt, as defined in the classification of world mountain regions by
Körner etal. (2017). This resulted ina distinction of 10major bi-
omes(FigureS4.5inAppendixS4),whoseshapefileisfreelyavail-
able(AppendixS5).
2.6 | Traitinformation
Tobroaden thepotentialapplicationsoftheglobalvegetationda-
tabaseinfunctionalcontexts,welinkedsPlottoTRY.Weaccessed
planttrait datafromTRYversion3.0onAugust10, 2016,and in-
cluded 18 traits that describe the leaf, wood and seed economics
spectra(Westoby,1998;Reich,2014;Table S6.4in AppendixS6),
andareknowntoaffectdifferentkeyecosystemprocessesandto
respond to macroclimatic drivers. These traits were represented
across all species in the TRYdatabase by at least 1,000 trait re-
cords.Weexcluded trait recordsfrom manipulative experiments
and outliers (Kattgeetal., 2011),which resulted in a matrix with
632,938 individual plant records on 52,032 taxa in TRY, having
data records for an average of 3.08 of the 18 selected traits. On
average,eachtraithasbeenmeasuredatleastoncein17.1%ofall
taxa.Inordertoattaindataforthese18traitsforallspecieswith
atleastonetraitvalueinTRY,weemployedhierarchicalBayesian
modelin g,usingtheRpackage‘BHPMF’(Fazayeli,Banerjee,Kattge,
Schrodt , & Reich, 2017; Schrodt eta l., 2015), to fill a gap i n the
matrixofindividualplantrecordsinTRY.Gapfillingallowsobtain-
ingtraitvaluesforaspeciesonwhichthistraithasnotbeenmeas-
ured,butforwhichothertraitsareavailable.Toassessgap-filling
quality,we usedtheprobability density distributions provided by
BHPMFforeachimputationandremovedhighlyuncer tainimputa-
tions with a coefficient of variation >1. We then loge- transformed
allgap-filledtraitvaluesandaveragedeachtraitbytaxon.Fortaxa
recorded at genus level only, we calculated genus means, result-
ing in a full trait matrix for 26,632 out of the 54,519 taxa in sPlot
(45.9%),with6,1,510and25,116taxaatthefamily,genusandspe-
cieslevel,respectively.Thesespeciescovered88.7%ofallspecies-
by-plotcombinations.
Foreverytraitjan dplotk, we cal culated the comm unity- we ighted
mean (CWM) and the community- weighted variance (CW V) for each
ofthe18traitsinaplot(Enquistetal.,2015):
where nkisthe number ofspecieswith trait informationin plotk,
pi,kisthe relativeabundanceofspecies iinplot k calculated as the
species’fractionincoverorabundanceoftotalcoverorabundance,
and ti,jis themean value of species i for trait j. CWMs and CWVs
werecalculated for18traitsin 1,117,369and 1,099,463 plots, re-
spectively,thesecondbeing asmallernumberasat least two taxa
were needed for CWV calculation.
3 | CONTENTOFSPLOT2.1
3.1 | Plotcommunitydata
sPlot 2.1 cont ains 1,121,244 veget ation plots fr om 160 countries
and from all continents (Figure3). The global coverage is biased
towards Euro pe, North Ame rica and Austr alia, reflecti ng unequal
samplin g effort ac ross the globe ( Table1). At the ecoregion l evel,
major gapsoccurin the wettropicsofSouth AmericaandAsia,as
wellasinsubtropicaldesertsworldwideandintheNorthAmerican
taiga.Although the plotsarehighlyclusteredgeographically,their
coverageintheenvironmentalspaceismuchmorerepresentative:
thehighestconcentrationofplotsisfoundinenvironmentsthatare
mostabundantglobally(Figure2),whiletheyarelackinginthevery
moistpartsoftheenvironmentalspace,whicharealsospatiallyrare,
andintheverycoldparts,whicharesparselyvegetated.
Inmostcases(98.4%),plotrecordsinsPlotincludefullspecies
lists of vascularplants,while1.6%hadonly wood species above
acertain diameter oronly the most dominant species recorded.
Terricolous bryophytes and lichens were additionally identified
in 14% and 7% of plots, respectively (Table S2.1 in Appendix
S2). Forest an d non-fores t plots comprise 3 30,873 (29.7%) and
513,035(46.0%) ofall plotsinsPlot,respectively.Inmostcases,
species abundancewasestimated usingdifferentvariantsofthe
Braun-Blanquet cover–abundance scale (66%), followed by per-
centage cover (15%)and 55other numeric or ordinalscales.The
temporalextent ofthe dataspansfrom1885to2015,but>94%
of vegetatio n plots were recor ded later than 1960 (Figur e S2.1
in Appen dix S2). Almost al l plots are geor eferenced (1,120,68 6)
andthemajorityofplotshavelocationuncertaintyof10morless
(FigureS2.2inAppendixS2).
Vascular plant richness per plot ranges from 1 to723species
(median=17species).Themostfrequentrichnessclassisbetween
20and25species(FigureS2.3inAppendixS2).Plotsizeisrepor ted
in65.4% of plots,ranging from<1m2 to 25 ha, with a median of
36 m2.Whileforestplotshaveplotsizes≥100m2, and in most cases
≤1,000m2,non-forestplotsrange between5 and100m2 (Figure
S2.4 in Appendix S2).Whenusing these size ranges,forest plots
tendtobericherinspecies (Figure4a). Thefact thatthegradient
inrichnessfoundinourplotswas atleast oneorderofmagnitude
stronger than differences that could be expected by the differ-
encesinplotsizepromptedus toproducethefirst globalmapsof
plot-scalespecies richness,separatelyforforestsandnon-forests
(Figure4a).Whileplotswithcompletevascularspeciescomposition
arelargelylackingfromthe wettropics,fortheremainingbiomes
theplot-scalerichnessdatadonotshowthetypicallatitudinalrich-
nessgradientin either formation. Particularlyspecies-rich forests
arefoundinthe wetsubtropics (suchas SE UnitedStates,Taiwan
andtheEastcoastofAustralia)aswellasinsomemountainousre-
gionsofthenemoralandsteppicbiomesofEurasia.Likewise,non-
forest communities have a particularly high mean vascular plant
speciesinmountainousregionsofthenemoralandsteppicbiomes
of Eurasia.
CWM
j,k=
n
k
i
pi,kti,
j
CWV
j,k=
n
k
i
pi,k(ti,jCWMj,k)
2
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FIGURE3 Global coverage of sPlot 2.1. (a) Contributing databases identified by different colours with indication of the two data
aggregators(EVA,TAVA)andafewparticularlylargeindividualdatabases;(b)availableplotnumbersperWWFEcoregion;and(c)available
plotdensityingridcellsof100km×100km
    
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FIGURE4 Examplesofglobalcommunity-levelpatternsthatcanbederivedfrom(a)sPlotaloneand(b–d)sPlotcombinedwithTRY,
hereshownasrawdataaveragedby1-degreegridcells.Thereareonlyaveryfewcells(142outof2633)comprisingonlyasingleplot.For
themaps,onlyplotswithfullvascularspeciescompositionandspatialaccuracy<5kmwereused.Theyarebasedon148,474and218,051
plotsforforestsandnon-forests,respectively.Notethatthesemapsarenotcorrectedforbiasescausedbythefactsthatnotallcommunity
typeswererecordedinallgridcellsandthatplotsizesaswellasthefractionofspecieswithavailabletraitdatavariedspatially.Mapsshow
patternsof(a)fine-grainalphadiversity,expressedasvascularplantspeciesrichness(onlyplotswithplotsizesof100–1000m²forforests
and5–100m²fornon-forests);(b)community-weightedmeans(CWMs)forloge-transformedtraitvaluesofspecificleafarea(SLA,m2/kg);
(c)plantheight(m);and(d)seedmass(mg)
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3.2 | Phylogeneticinformation
Thephylogenetic tree for sPlot was produced from 53,489vascu-
larplantnamescontainedinthedatabase,comprising5518genera
(AppendixS7).ModeratelytohighlyfrequentspeciesinsPlot2.1are
equally distributedacross thephylogeny (correspondingtoyellow-
ishto reddishcolors for lowandhigh peaks,respectively,inFigure
S7.6in Appendix S7). Coverage ofspecies included inthe phylog-
eny ranges f rom 89% of species t hat occur only once i n all plots
to100% of specieswith a frequency>10,000 plots(FigureS7.7in
AppendixS7).
3.3 | Functionalinformation
Theproportionofspecies withtraitinformationincreaseswiththe
species’ frequency in plots. Gap-filled trait information isavailable
for77.2%and96.2%fortaxathat occurred in morethan 100and
1,000 plots, respectively. Trait coverage is similar across biomes
(Figure S8.8 inAppendix S8).Acrossall biomes, the proportion of
speciesforwhichgap-filledtraitdataareavailableincreaseswiththe
species’ frequency across plots. Compared to gap-filled data, trait
coverage for the original trait data is considerably lower, being high-
estforheight,seedmass,leafareaandspecificleafarea(SLA,Figure
S8.9inAppendixS8).
Thehighrepresentationofthe18traitsinthegap-filledtraitdata
andthehighdegreeoftraitcoverageforfrequentspeciesacrossall
biomes(>75%)madeusconfidenttoproducethefirstmapsofglobal
patternsofcommunity-weightedmeans(CWMs)(Figure4b–d).The
mapsshowthemaintraitdimensionsofSLA,heightandseedmass,
separatelyforforestsandnon-forests,forthoseregionsoftheworld
that are already sufficiently covered by sPlot data. Accordingly,
CWMsofSLAarequitesimilarforforestandnon-forestplots,being
highest inwestern North Americaand Europe andlowestin east-
ernNorthAmerica,EastandSouthAustralia(Figure4b).Non-forest
vegetation shows lowest CWMs of SLA in the deser t regions of
theNamibandSinai. Forestswith highestCWMs ofcanopyheight
are foundalongthe western and eastern coast of NorthAmerica,
someregionsinEurope,EastAsiaandsouthernAustralia(Figure4c).
Theseareasonlypartlycoincidewiththoseofhighestseedmasses
for forests, while seed mass in non- forests is highest in the east-
ernMediterraneanBasinandinCentralAsia(Figure4d).Thecorre-
spondingpatterns for CWVareshowninFigureS9.10inAppendix
S9.
4 | DATAUSAGE
ThesPlotdatabase(thevegetation-plotdata,includingtheenviron-
mental informationfor eachplot andthe species phylogeny) is re-
leased in fixedversionstoallowreproducibility ofresults, butalso
due to the enormous effort needed for data integration and harmo-
nizationandforupdatingthephylogeny.Bydeliveringfewfixedver-
sions whil e keeping older ve rsions availab le, the sPlot con sortium
ensuresthatthesamedatacanbeusedinparallelprojectsandthat
thedataunderlyingaspecificstudyremainaccessibleinthefuture,
thus allowing re- analysis. Each new version will be matched to the
currentTRYdatabase.
Data access to sPlot is regulated by the Governance and Data
PropertyRules(www.idiv.de/sPlot)toensureafairbalancebetween
the interests of data contributors and data analysts. In brief, the
sPlotRulesstatethat:(a)allcontributingvegetation-plotdatabases
becomemembersofthesPlotconsortium,representedbytheircus-
todian and d eputy cus todian; (b) vegetati on-pl ot data contrib uted
to sPlot rema in the proper ty of the dat a contributor s and can be
withdrawnat any time exceptfor approved projects;(c)othersci-
entists(e.g.,datamanagersorparticipantsofthesPlotworkshops)
with par ticular resp onsibilities m ay also be appointe d as personal
members to the sPlot consortium; (d) sPlot data can be requested
forprojects that involveatleastone member of the sPlot consor-
tium;(e)wheneveraprojecthasbeenproposed,allsPlotconsortium
members will be informed and can declare their interest in becoming
co-authors of manuscriptsresultingfrom thisprojectandthen be-
coming actively involved in data evaluation and writing; and (f) if also
thematchedgap-filledororiginaltraitdatafromTRYarerequested
foraproject,likewisemembersfromtheTRYconsortiumcanopt-in
as co- authors. The sPlot database is, therefore, available according
toa‘give-and-receive’system. Moreover,the dataare available to
any researcher by establishing a collaboration that includes and is
supportedbyatleastonesPlotconsortiummember.
The sPlot consortium is governed by a Steering Committee
elected by all consortium members for two- year, renewable terms.
Project proposals can be submitted to the Steering Committee,
whichensuresthatthesPlotRulesarefollowedandredundantwork
between overlapping projects is avoided. The lists of databases,
sPlotconsortiummembers and the SteeringCommitteemembers
areupdatedregularlyonthesPlotwebsite,asarethesPlotRulesand
thelistofapprovedprojects.
5 | EXPECTEDIMPACTANDLIMITATIONS
The main ai m of the sPlot databa se is to catalyze a co llaborative
networkforunderstandingglobaldiversitypatternsofplantcom-
munities in space and time. sPlot provides a unique, integrated
global re pository of data t hat would otherw ise be fragmente d in
unconnected and structurally inconsistent databases at regional,
national o r continental l evels. Together with th e provision of har-
monized phylogenetic, functional and environmental information,
sPlotallows,for the first time, globalanalysesofplant community
data.Comparedtoapproachesusing dataaggregatedfromspecies
occurrences in grid cells, sPlot will significantly advance ecological
analysesandfutureinterdisciplinaryresearchinatleastfourdiffer-
ent ways.
1. UsingsPlot,onecanpredictthespeciesthatcanco-existin
a community and also the frequencies of their co-occurrence
    
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BRUELHEID E Et aL.
(Breitschwerdt, Jandt, & Bruelheide, 2015) or niche overlap
(Broennimann et al., 2012). In addition, emerging tools such as
Markovnetworkscan be usedtoinferstrengths ofinterspecific
interactions (Harris, 2016). When investigating community as-
sembly rules, the same information can be used to derive
species poolsforspecificvegetation types(deBelloetal.,2016;
Karger etal., 2016; Lewis, Szava-Kovats, & Pärtel, 2016).
Moreover, the co-occurrence data from sPlot can be used to
address fundamental patterns and drivers of plant invasions
better than information on large geographic entities (e.g., van
Kleunen etal., 2015) alone could.
2. sPlotdatacanbeaggregatedacrossalltypesofplots,bygrid
cells,ecoregions,environment,orevenvegetationtypeorforma-
tion .Fur t hermore,replicatedplotswithi ngridcells,eco regio ns,or
any other subdivision of environmental conditions or vegetation
ty pesal lowuserstoderivemea suresofcompositiona ldifferences
between plant communities within grid cells (= beta diversity;
Table1).Thus,thecommunitydataareanimportantcomplement
to regional-scale species occurrence data (e.g., Enquist etal.,
2016;Kreft&Jetz,2007).
3. sPlotdataprovideinformationontheproportionofspeciesina
community (in terms of cover, basal area, frequency). When com-
bine dwithfuncti onalt rai tinformati on,relativeabundanceofspe-
cies allows calculation of community abundance-weighted mean
trait values (Bruelheide et al., 2018). Information on the relative
contributionofspeciesto acommunity-aggregated traitvalue is
particularlynecessarywhentraitsareusedasproxiesforvegeta-
tionf uncti onsandp roce sses ,all owi ngt ote s t,am ongothe rthi ngs,
themassratiohypothesis(Garnieretal.,2004;Grime,1998)and
toassess the roles of divergent traits (Díaz etal., 2007;Kröber
et al., 2015).
4. Pla ntspecieswit hinplot sca nbelinkedtotr ait sthatpr edictinter-
actionswith organisms from other trophic groups, both below-
ground (mycorrhizae, soil decomposers) and above-ground
(herbivoresandpollinators).Thiswillallowlinkingvegetationplot
informat ion to ecosystem pro cesses and ser vices such as pest
control,pollinationandnutrientcycling(e.g.,deBelloetal.,2010).
Despite the largeamount ofavailabledataand itspotential suit-
ability for global research, a number of limitations must be considered
by future users of sPlot, such as (a) biases towards certain regions and
communities,(b) near-completelackofplots with completevascular
plant spe cies compositio n for certain re gions (e.g., the wet t ropics),
(c)identificationorsampling errorsbythesurveyorsandincomplete
records becausethe detectionofsome species maybe precluded in
certainseasonsbytheirphenology,(d)taxonomicuncertainty,particu-
larlyinthetropics,(e)stronglyvaryingplotsizesemployedindifferent
studiesand regions,(f)lackof traitmeasuresattheplotlevel.Forex-
ample,patternsofdiversitycomponent saretypicallyaffectedbygrain
size.ThismeansthatusingsPlot datafor suchstudieseitherrequires
filtering for plots with identical or at least similar size or accounting
forthe plot-sizeeffects in thestatisticalmodel.Inaddition, analyses
of functional diversity with sPlot data are limited by the absence of
trait datafor a (small)portion of thespeciesand by thelack of plot-
specifictraitmeasures.Furthermore,thenon-randomandgeographi-
callyandecologicallyveryunequaldistributionoftheplotscontained
in sPlot ca ll for stratif ied resamp ling to balance re cords of differ ent
environments(e.g.,stratifiedbyclimate,Figure2)orphysiognomicfor-
mations(Figure4).UsersofsPlotneedtobeawareoftheseandother
limitations and to correctpotential biases for their specific research
question.
6 | CONCLUSION
sPlotisa uniqueglobaldatabaseofplantcommunityrecordssam-
pledwithrelativelysimilarmethodswidelyusedinvegetationecol-
ogy. The integration of co- occurrence data into a unified database
thatcanbedirectlylinkedtoenvironmental,functionalandphyloge-
neticinformation,makessPlotanunprecedentedandessentialtool
foranalyzingglobal plant diversity,thestructureofplantcommu-
nities andtheco-occurrence of plantspecies.The compatibility of
this consolidated database with other global databases, e.g., via a
jointtaxonomicbackbonewithTRYandtheGlobalNaturalizedAlien
Flora (GloNAF;van Kleunenetal.,2015) (via taxon names), or via
standardizedgeo-referencewithdatabasesofenvironmental infor-
mation suchasCHELSA, WorldClim or SoilGrids(Bruelheide etal.,
2018),facilitatesdata integration andcreatesnew researchoppor-
tunities.Theadaptivemanagementofthedatabaseemployedbythe
sPlotconsortiumallowsregularincorporationofnewdata,resulting
inadynamic platform forstoring and analyzingthe most compre-
hensivecompilationofplantcommunitydataworldwide.
ACKNOWLEDGEMENTS
We are grateful to thousands of vegetation scientists who sam-
pled vegetation plots inthe field ordigitizedtheminto regional,
national or international databases. We also appreciate the sup-
portoftheGermanResearchFoundationforfundingsPlotasone
oft heiDiv(DFGFZT118)researchplatforms,andtheorg anization
ofthree workshops through thesDivcalls.Weacknowledgethis
supportwithnamingthedatabase“sPlot”,wherethe“s”refersto
the sDiv s ynthesis work shops. The st udy was suppor ted by the
TRYinitiative on plant traits (http://www.try-db.org).For allfur-
theracknowledgementsseeAppendixS10.WethankMeelisPärtel
forhisveryfastandconstructivefeedbackonanearlierversionof
thismanuscript.
AUTHOR CONTRIBUTIONS
H.Bru. had the original idea and led the consortium from the start,
whileO.Pu. and J.D. coordinated the sPlotworkshops.J.D.,S.M.H.
andU.J.compiledthedatabasestobeincludedinsPlot.J.D.andlater
B.J.-A. and F.M.S.coordinated thenetworkand the database.O.P.
preparedthetaxonomicandphylogeneticdata.S.M.Hprogrammed
theTurbovegsoftware.B.Sa.,F.J.,H.Bru.,J.D.,J.K.,M.Ch.,andV.D.P.
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organizedthenetworkintheSteeringCommittee.B.J.-A.andH.Bru.
led the writing together with J.D. and input from S.M.H., O.Pu.,
M.Ch., F.J., J.K., V.D.P.,B.Sa.,I.Au., I.B., R.K.P.,R.F.,S.H., U.J., J.L.,
G.P.,F.M.S.,M.S.,F.S.andM.W.Therestofauthors(orderedalpha-
betically)contributedtheplotandtraitdata.Allauthorsagreedwith
thefinalmanuscript.
DATA ACCE SSIB ILIT Y
ThedatacontainedinsPlot (thevegetation-plotdatacomplemented
byspeciesphylogenyandenvironmentalinformation)areavailableon
request, through contacting any of the consortium members for sub-
mittin gapape rpr opos al.Thep rop osal ssh ouldfollowtheGovern ance
andDataPropertyRulesofthesPlotWorkingGroup,whichareavail-
ableonthesPlotwebsite(www.idiv.de/sPlot).Afteracceptance, the
respectivedatawillbeprovided.Inadditiontotheplotdata,CWMs
andCWVsof18planttraitsareavailableforeveryplot.
ORCID
Helge Bruelheide https://orcid.org/0000-0003-3135-0356
Jürgen Dengler https://orcid.org/0000-0003-3221-660X
Borja Jiménez-Alfaro https://orcid.org/0000-0001-6601-9597
Oliver Purschke https://orcid.org/0000-0003-0444-0882
Milan Chytrý https://orcid.org/0000-0002-8122-3075
Valério D. Pillar https://orcid.org/0000-0001-6408-2891
Jens Kattge https://orcid.org/0000-0002-1022-8469
Idoia Biurrun https://orcid.org/0000-0002-1454-0433
Richard Field https://orcid.org/0000-0003-2613-2688
Jonathan Lenoir https://orcid.org/0000-0003-0638-9582
Robert K. Peet https://orcid.org/0000-0003-2823-6587
Francesco Maria Sabatini https://orcid.
org/0000-0002-7202-7697
Marco Schmidt https://orcid.org/0000-0001-6087-6117
Franziska Schrodt https://orcid.org/0000-0001-9053-8872
Emiliano Agrillo https://orcid.org/0000-0003-2346-8346
Miguel Alvarez https://orcid.org/0000-0003-1500-1834
Pierangela Angelini https://orcid.org/0000-0002-5321-9757
Mohammed A. S. Arfin Khan https://orcid.
org/0000-0001-6275-7023
Fabio Attorre https://orcid.org/0000-0002-7744-2195
Michael Beckmann https://orcid.org/0000-0002-5678-265X
Yves Bergeron https://orcid.org/0000-0003-3707-3687
Erwin Bergmeier https://orcid.org/0000-0002-6118-4611
Zoltán Botta-Dukát https://orcid.org/0000-0002-9544-3474
Chaeho Byun https://orcid.org/0000-0003-3209-3275
Laura Casella https://orcid.org/0000-0003-2550-3010
Luis Cayuela https://orcid.org/0000-0003-3562-2662
Tomáš Černý https://orcid.org/0000-0003-2637-808X
Victor Chepinoga https://orcid.org/0000-0003-3809-7453
János Csiky https://orcid.org/0000-0002-7920-5070
Els De Bie https://orcid.org/0000-0001-7679-743X
Michele De Sanctis https://orcid.org/0000-0002-7280-6199
Jaime Fagúndez https://orcid.org/0000-0001-6605-7278
Xavier Font https://orcid.org/0000-0002-7253-8905
Estelle Forey https://orcid.org/0000-0001-6082-3023
André Luis Gasper https://orcid.org/0000-0002-1940-9581
Alvaro G. Gutierrez https://orcid.org/0000-0001-8928-3198
Tianhua He https://orcid.org/0000-0002-0924-3637
Pedro Higuchi https://orcid.org/0000-0002-3855-555X
Norbert Hölzel https://orcid.org/0000-0002-6367-3400
Steven Jansen https://orcid.org/0000-0002-4476-5334
Martin Jiroušek https://orcid.org/0000-0002-4293-478X
Norbert Jürgens https://orcid.org/0000-0003-3211-0549
Ali Kavgacı https://orcid.org/0000-0002-4549-3668
Elizabeth Kearsley https://orcid.org/0000-0003-0046-3606
Michael Kessler https://orcid.org/0000-0003-4612-9937
Ingolf Kühn https://orcid.org/0000-0003-1691-8249
Flavia Landucci https://orcid.org/0000-0002-6848-0384
Ching-Feng Li https://orcid.org/0000-0003-0744-490X
Peter Manning https://orcid.org/0000-0002-7940-2023
Corrado Marcenò https://orcid.org/0000-0003-4361-5200
Maurizio Mencuccini https://orcid.org/0000-0003-0840-1477
Vanessa Minden https://orcid.org/0000-0002-4933-5931
Jesper Erenskjold Moeslund https://orcid.
org/0000-0001-8591-7149
Marco Moretti https://orcid.org/0000-0002-5845-3198
Jérôme Munzinger https://orcid.org/0000-0001-5300-2702
Ülo Niinemets https://orcid.org/0000-0002-3078-2192
Arkadiusz Nowak https://orcid.org/0000-0001-8638-0208
Gerhard E. Overbeck https://orcid.org/0000-0002-8716-5136
Wim A. Ozinga https://orcid.org/0000-0002-6369-7859
Hristo Pedashenko https://orcid.org/0000-0002-6743-0625
Josep Peñuelas https://orcid.org/0000-0002-7215-0150
Aaron Pérez-Haase https://orcid.org/0000-0002-5974-7374
Petr Petřík https://orcid.org/0000-0001-8518-6737
Oliver L. Phillips https://orcid.org/0000-0002-8993-6168
Cyrus Samimi https://orcid.org/0000-0001-7001-7893
Jozef Šibík https://orcid.org/0000-0002-5949-862X
Željko Škvorc https://orcid.org/0000-0002-3052-699X
    
|
 181
Journal of Vegetation Science
BRUELHEID E Et aL.
Jens-Christian Svenning https://orcid.
org/0000-0002-3415-0862
Grzegorz Swacha https://orcid.org/0000-0002-6380-2954
Emin Ugurlu https://orcid.org/0000-0003-0824-1426
Eduardo Vélez-Martin https://orcid.org/0000-0001-8028-8953
Roberto Venanzoni https://orcid.org/0000-0002-7768-0468
Risto Virtanen https://orcid.org/0000-0002-8295-8217
Evan Weiher https://orcid.org/0000-0002-5375-9964
Timothy Whitfeld https://orcid.org/0000-0003-1850-6432
Susan Wiser https://orcid.org/0000-0002-8938-8181
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