# sPlotOpen – An environmentally balanced, open-access, global dataset of vegetation plots

Francesco Maria Sabatini, Jonathan Roger Michel Henri Lenoir, Tarek

Hattab, Elise Aimee Arnst, Milan Chytrý, Jürgen Dengler, Patrice de Ruffray,

Stephan Hennekens, Ute Jandt, Florian Jansen, et al.

<figure><img src="image_1.png" /></figure>

## ► To cite this version:

Francesco Maria Sabatini, Jonathan Roger Michel Henri Lenoir, Tarek Hattab, Elise Aimee Arnst, Milan Chytrý, et al.. sPlotOpen – An environmentally balanced, open-access, global dataset of vegetation plots. Global Ecology and Biogeography, 2021, 30 (9), pp.1740-1764. 10.1111/geb.13346. hal-03286084

HAL Id: hal-03286084

https://hal.science/hal-03286084

Submitted on 8 Sep 2021

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<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_3.png" /></figure>

# sPlotOpen – An environmentally balanced, open-access, global dataset of vegetation plots

Francesco Maria Sabatini¹,² ID | Jonathan Lenoir³ ID | Tarek Hattab⁴ ID |
Elise Aimee Arnst⁵ ID | Milan Chytrý⁶ ID | Jürgen Dengler¹,⁷,⁸ ID | Patrice De Ruffray⁹ |
Stephan M. Hennekens¹⁰ ID | Ute Jandt¹,² ID | Florian Jansen¹¹ ID |
Borja Jiménez-Alfaro¹² ID | Jens Kattge¹³ ID | Aurora Levesley¹⁴ ID | Valério D. Pillar¹⁵ ID |
Oliver Purschke¹⁶ ID | Brody Sandel¹⁷ ID | Fahmida Sultana¹⁸ ID | Tsipe Aavik¹⁹ ID |
Svetlana Aćić²⁰ ID | Alicia T. R. Acosta²¹ ID | Emiliano Agrillo²² ID | Miguel Alvarez²³ ID |
Iva Apostolova²⁴ ID | Mohammed A. S. Arfin Khan²⁵ ID | Luzmila Arroyo²⁶ ID | Fabio Attorre²⁷ ID |
Isabelle Aubin²⁸ ID | Arindam Banerjee²⁹ ID | Marijn Bauters³⁰,³¹ ID |
Yves Bergeron³² ID | Erwin Bergmeier³³ ID | Idoia Biurrun³⁴ ID | Anne D. Bjorkman³⁵,³⁶ ID |
Gianmaria Bonari³⁷ ID | Viktoria Bondareva³⁸ ID | Jörg Brunet³⁹ ID | Andraž Čarni⁴⁰,⁴¹ ID |
Laura Casella⁴² ID | Luis Cayuela⁴³ ID | Tomáš Černý⁴⁴ ID | Victor Chepinoga⁴⁵ ID |
János Csiky⁴⁶ ID | Renata Ćušterevska⁴⁷ ID | Els De Bie⁴⁸ ID | André Luis de Gasper⁴⁹ ID |
Michele De Sanctis²⁷ ID | Panayotis Dimopoulos⁵⁰ ID | Jiri Dolezal⁵¹ ID | Tetiana Dziuba⁵² ID |
Mohamed Abd El-Rouf Mousa El-Sheikh⁵³,⁵⁴ ID | Brian Enquist⁵⁵ ID | Jörg Ewald⁵⁶ ID |
Farideh Fazayeli⁵⁷,⁵⁸ ID | Richard Field⁵⁹ ID | Manfred Finckh⁶⁰ ID | Sophie Gachet⁶¹ ID |
Antonio Galán-de-Mera⁶²,⁶³,⁶⁴ ID | Emmanuel Garbolino⁶⁵ ID | Hamid Gholizadeh⁶⁶ ID |
Melisa Giorgis⁶⁷ ID | Valentin Golub⁶⁸ ID | Inger Greve Alsos⁶⁹ ID | John-Arvid Grytnes⁷⁰ ID |
Gregory Richard Guerin⁷¹ ID | Alvaro G. Gutiérrez⁷² ID | Sylvia Haider¹,² ID |
Mohamed Z. Hatim⁷³,⁷⁴ ID | Bruno Hérault⁷⁵,⁷⁶,⁷⁷ ID | Guillermo Hinojos Mendoza⁷⁸ ID |
Norbert Hölzel⁷⁹ ID | Jürgen Homeier⁸⁰ ID | Wannes Hubau⁸¹,⁸² ID | Adrian Indreica⁸³ ID |
John A. M. Janssen⁸⁴ ID | Birgit Jedrzejek⁷⁹ ID | Anke Jentsch⁸⁵ ID | Norbert Jürgens⁶⁰ ID |
Zygmunt Kącki⁸⁶ ID | Jutta Kapfer⁸⁷ ID | Dirk Nikolaus Karger⁸⁸ ID | Ali Kavgaci⁸⁹ ID |
Elizabeth Kearsley⁹⁰ ID | Michael Kessler⁹¹ ID | Larisa Khanina⁹² ID | Timothy Killeen⁹³ ID |
Andrey Korolyuk⁹⁴ ID | Holger Kreft⁹⁵ ID | Hjalmar S. Kühl¹,⁹⁶ ID | Anna Kuzemko⁹⁷ ID |
Flavia Landucci⁶ ID | Attila Lengyel⁹⁸ ID | Frederic Lens⁹⁹,¹⁰⁰ ID |
Débora Vanessa Lingner¹⁰¹ ID | Hongyan Liu¹⁰² ID | Tatiana Lysenko¹⁰³,¹⁰⁴,¹⁰⁵ ID |
Miguel D. Mahecha¹,¹⁰⁶ ID | Corrado Marcenò⁶,³⁴ ID | Vasiliy Martynenko¹⁰⁷ ID |
Jesper Erenskjold Moeslund¹⁰⁸ ID | Abel Monteagudo Mendoza¹⁰⁹,¹¹⁰ ID |

<footnote>Francesco Maria Sabatini and Jonathan Lenoir contributed equally to this work.</footnote>

<footnote>This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.</footnote>

<footnote>© 2021 The Authors. *Global Ecology and Biogeography* published by John Wiley & Sons Ltd.</footnote>
Ladislav Mucina¹¹¹,¹¹² ID | Jonas V. Müller¹¹³ ID | Jérôme Munzinger¹¹⁴ ID |

Alireza Naqinezhad¹¹⁵ | Jalil Noroozi¹¹⁶ ID | Arkadiusz Nowak¹¹⁷,¹¹⁸ ID |

Viktor Onyshchenko¹¹⁹ | Gerhard E. Overbeck¹²⁰ ID | Meelis Pärtel<fnref n="19" /> ID |

Aníbal Pauchard¹²¹,¹²² ID | Robert K. Peet¹²³ ID | Josep Peñuelas¹²⁴,¹²⁵ ID |

Aaron Pérez-Haase¹²⁶,¹²⁷ ID | Tomáš Peterka<fnref n="6" /> ID | Petr Petřík¹²⁸ ID | Gwendolyn Peyre¹²⁹ ID |

Oliver L. Phillips<fnref n="14" /> ID | Vadim Prokhorov¹³⁰ ID | Valerijus Rašomavičius¹³¹ ID |

Rasmus Revermann¹³²,¹³³ ID | Gonzalo Rivas-Torres¹³⁴ ID | John S. Rodwell¹³⁵ ID |

Eszter Ruprecht¹³⁶ ID | Solvita Rūsiņa¹³⁷ ID | Cyrus Samimi¹³⁸ ID | Marco Schmidt¹³⁹ ID |

Franziska Schrodt⁵⁹ ID | Hanhuai Shan¹⁴⁰ ID | Pavel Shirokikh¹⁰⁷ ID | Jozef Šibík¹⁴¹ ID |

Urban Šilc¹⁴² ID | Petr Sklenář¹⁴³ ID | Željko Škvorc¹⁴⁴ ID | Ben Sparrow¹⁴⁵ ID |

Marta Gaia Sperandii<fnref n="21" />,¹⁴⁶ ID | Zvjezdana Stančić¹⁴⁷ ID | Jens-Christian Svenning¹⁴⁸ ID |

Zhiyao Tang¹⁰² ID | Cindy Q. Tang¹⁴⁹ ID | Ioannis Tsiripidis¹⁵⁰ ID | Kim André Vanselow¹⁵¹ ID |

Rodolfo Vásquez Martínez¹⁰⁹ ID | Kiril Vassilev<fnref n="24" /> ID | Eduardo Vélez-Martin¹⁵² ID |

Roberto Venanzoni¹⁵³ ID | Alexander Christian Vibrans¹⁰¹ ID | Cyrille Violle¹⁵⁴ ID |

Risto Virtanen<fnref n="1" />,¹⁵⁵,¹⁵⁶ ID | Henrik von Wehrden¹⁵⁷ ID | Viktoria Wagner¹⁵⁸ ID |

Donald A. Walker¹⁵⁹ ID | Donald M. Waller¹⁶⁰ ID | Hua-Feng Wang¹⁶¹ ID |

Karsten Wesche<fnref n="1" />,¹⁶²,¹⁶³ ID | Timothy J. S. Whitfeld¹⁶⁴ ID | Wolfgang Willner¹¹⁶ ID |

Susan K. Wiser<fnref n="5" /> ID | Thomas Wohlgemuth¹⁶⁵ ID | Sergey Yamalov¹⁶⁶ ID |

Martin Zobel<fnref n="19" /> ID | Helge Bruelheide<fnref n="1" />,<fnref n="2" /> ID |

<footnote n="1">¹German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany</footnote>

<footnote n="2">²Institute of Biology, Martin-Luther University Halle-Wittenberg, Halle, Germany</footnote>

<footnote n="3">³UMR CNRS 7058 "Ecologie et Dynamique des Systèmes Anthropisés" (EDYSAN), Université de Picardie Jules Verne, Amiens, France</footnote>

<footnote n="4">⁴MARBEC, Univ Montpellier, CNRS, IFREMER and IRD, Sète, France</footnote>

<footnote n="5">⁵Manaaki Whenua - Landcare Research, Lincoln, New Zealand</footnote>

<footnote n="6">⁶Department of Botany and Zoology, Masaryk University, Brno, Czech Republic</footnote>

<footnote n="7">⁷Vegetation Ecology Group, Institute of Natural Resource Sciences (IUNR), Zurich University of Applied Sciences (ZHAW), Wädenswil, Switzerland</footnote>

<footnote n="8">⁸Bayreuth Center of Ecology and Environmental Research (BayCEER), University of Bayreuth, Bayreuth, Germany</footnote>

<footnote n="9">⁹Institut de biologie moléculaire des plantes-CNRS, Université de Strasbourg, Strasbourg, France</footnote>

<footnote n="10">¹⁰Wageningen Environmental Research, Wageningen, the Netherlands</footnote>

<footnote n="11">¹¹Faculty of Agricultural and Environmental Sciences, University of Rostock, Rostock, Germany</footnote>

<footnote n="12">¹²Research Unit of Biodiversity (CSIC/UO/PA), University of Oviedo, Mieres, Spain</footnote>

<footnote n="13">¹³Max Planck Institute for Biogeochemistry, Jena, Germany</footnote>

<footnote n="14">¹⁴School of Geography, University of Leeds, Leeds, UK</footnote>

<footnote n="15">¹⁵Department of Ecology, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil</footnote>

<footnote n="16">¹⁶Institute for Medical Epidemiology, Biometrics and Informatics (IMEBI), Interdisciplinary Center for Health Sciences, Medical School of the Martin-Luther University Halle-Wittenberg, Halle/Saale, Germany</footnote>

<footnote n="17">¹⁷Department of Biology, Santa Clara University, Santa Clara, California, USA</footnote>

<footnote n="18">¹⁸Shahjalal University of Science & Technology, Sylhet, Bangladesh</footnote>

<footnote n="19">¹⁹Institute of Ecology and Earth Sciences, University of Tartu, Tartu, Estonia</footnote>

<footnote n="20">²⁰Faculty of Agriculture, Department of Botany, University of Belgrade, Belgrade-Zemun, Serbia</footnote>

<footnote n="21">²¹Department of Sciences, Roma Tre University, Rome, Italy</footnote>

<footnote n="22">²²Biodiversity Conservation Department, ISPRA - Italian National Institute for Environmental Protection and Research, Rome, Italy</footnote>

<footnote n="23">²³University of Bonn, INRES, Bonn, Germany</footnote>

<footnote n="24">²⁴Department of Plant and Fungal Diversity and Resources, Institute of Biodiversity and Ecosystem Research, Bulgarian Academy of Sciences, Sofia, Bulgaria</footnote>

<footnote n="25">²⁵Shahjalal University of Science & Technology, Sylhet, Bangladesh</footnote>
<sup>26</sup>Dirección de la Carrera de Biología, Universidad Autónoma Gabriel René Moreno, Santa Cruz de la Sierra, Bolivia

<sup>27</sup>Department of Environmental Biology, Sapienza University of Rome, Rome, Italy

<sup>28</sup>Natural Resources Canada, Great Lakes Forestry Centre, Canadian Forest Service, Sault Ste Marie, Ontario, Canada

<sup>29</sup>Department of Computer Science, University of Illinois Urbana Champaign, Urbana, Illinois, USA

<sup>30</sup>Department Green Chemistry and Technology, Isotope Bioscience Laboratory (UGent-ISOFYS), Ghent University, Ghent, Belgium

<sup>31</sup>Department Environment, Computational and Applied Vegetation Ecology (UGent-CAVELab), Ghent University, Ghent, Belgium

<sup>32</sup>Forest Research Institute, Université du Québec en Abitibi-Témiscamingue, Rouyn-Noranda, Quebec, Canada

<sup>33</sup>Vegetation Ecology and Phytodiversity, University of Göttingen, Göttingen, Germany

<sup>34</sup>Plant Biology and Ecology, University of the Basque Country UPV/EHU, Bilbao, Spain

<sup>35</sup>Department of Biological and Environmental Sciences, University of Gothenburg, Gothenburg, Sweden

<sup>36</sup>Gothenburg Global Biodiversity Centre, Gothenburg, Sweden

<sup>37</sup>Free University of Bozen-Bolzano, Bolzano, Italy

<sup>38</sup>Laboratory of Phytodiversity Problem and of Phytocoenology, Institute of Ecology of the Volga River Basin, Toljatty, Russian Federation

<sup>39</sup>Southern Swedish Forest Research Centre, Swedish University of Agricultural Sciences, Alnarp, Sweden

<sup>40</sup>Institute of Biology, Research Center of the Slovenian Academy of Sciences and Arts, Ljubljana, Slovenia

<sup>41</sup>School for Viticulture and Enology, University of Nova Gorica, Nova Gorica, Slovenia

<sup>42</sup>Biodiversity Conservation Department, ISPRA - Italian National Institute for Environmental Protection and Research, Rome, Italy

<sup>43</sup>Department of Biology and Geology, Physics and Inorganic Chemistry, Universidad Rey Juan Carlos, Móstoles, Spain

<sup>44</sup>Department of Forest Ecology, Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Praha 6 - Suchdol, Czech Republic

<sup>45</sup>Central Siberian Botanical Garden SB RAS, Novosibirsk, Russian Federation

<sup>46</sup>Department of Ecology, University of Pécs, Pécs, Hungary

<sup>47</sup>Institute of Biology, Faculty of Natural Sciences and Mathematics, Ss. Cyril and Methodius University, Skopje, Republic of North Macedonia

<sup>48</sup>Biotope Diversity, Research Institute for Nature and Forest (INBO), Brussels, Belgium

<sup>49</sup>Universidade Regional de Blumenau, Blumenau, Brazil

<sup>50</sup>Laboratory of Botany, Division of Plant Biology, Department of Biology, University of Patras, Patras, Greece

<sup>51</sup>Department of Functional Ecology, Institute of Botany, Czech Academy of Sciences, Trebon, Czech Republic

<sup>52</sup>M.G. Kholodny Institute of Botany, National Academy of Sciences of Ukraine, Geobotany and Ecology, Kyiv, Ukraine

<sup>53</sup>Botany and Microbiology Department, College of Science, King Saud University, Riyadh, Saudi Arabia

<sup>54</sup>Botany Department, Faculty of Science, Damanhour University, Damanhour, Egypt

<sup>55</sup>Ecology and Evolutionary Biology, University of Arizona, Tucson, Arizona, USA

<sup>56</sup>Hochschule Weihenstephan-Triesdorf, University of Applied Sciences, Freising, Germany

<sup>57</sup>Google LLC, Mountain View, California, USA

<sup>58</sup>University of Minnesota - Twin Cities, Minneapolis, Minnesota, USA

<sup>59</sup>School of Geography, University of Nottingham, Nottingham, UK

<sup>60</sup>Biodiversity, Ecology and Evolution of Plants, Institute for Plant Science & Microbiology, University of Hamburg, Hamburg, Germany

<sup>61</sup>CNRS, IRD, IMBE, Aix Marseille Univ, Avignon Université, Marseille, France

<sup>62</sup>Laboratorio de Botánica, Universidad CEU San Pablo, Madrid, Spain

<sup>63</sup>Laboratorio de Botánica, Universidad Privada Antonio Guillermo Urrelo, Cajamarca, Peru

<sup>64</sup>Herbario AQP, Estudios Fitogeográficos del Perú, Paucarpata, Arequipa, Peru

<sup>65</sup>Nova Sophia - Regus Nova, Climpact Data Science (CDS), CS, Sophia Antipolis Cedex, France

<sup>66</sup>Department of Plant Biology, University of Mazandaran, Babolsar, Iran

<sup>67</sup>Ecología Vegetal y Fitogeografía, Instituto Multidisciplinario de Biología Vegetal (IMBIV-CONICET), Córdoba, Argentina

<sup>68</sup>Laboratory of Phytocoenology, Samara Federal Research Center of the Russian Academy of Sciences, Institute of Ecology of the Volga river basin of the Russian Academy of Science, Toljatty, Russian Federation

<sup>69</sup>The Arctic University Museum of Norway,UiT - The Arctic University of Norway, Tromsø, Norway

<sup>70</sup>Department of Biological Sciences, University of Bergen, Bergen, Norway

<sup>71</sup>School of Biological Sciences, University of Adelaide, Adelaide, Australia

<sup>72</sup>Departamento de Ciencias Ambientales y Recursos Naturales Renovables, Facultad de Ciencias Agronomicas, Universidad de Chile, Santiago, Chile

<sup>73</sup>Plant Ecology and Nature Conservation Group - Environmental Sciences Department, Wageningen University, Wageningen, the Netherlands

<sup>74</sup>Botany and Microbiology Department - Faculty of Science, Tanta University, Tanta, Egypt

<sup>75</sup>CIRAD, UPR Forêts et Sociétés, Yamoussoukro, Ivory Coast

<sup>76</sup>University of Montpellier, CIRAD, Montpellier, France

<sup>77</sup>INP-HB, Institut National Polytechnique Félix Houphouët-Boigny, Yamoussoukro, Ivory Coast
⁷⁸ASES Ecological and Sustainable Services, Aubenas, France

⁷⁹Institute of Landscape Ecology, University of Münster, Münster, Germany

⁸⁰Plant Ecology and Ecosystems Research, University of Göttingen, Göttingen, Germany

⁸¹Department Environment, Laboratory of Wood Biology (UGent-WoodLab), Ghent University, Ghent, Belgium

⁸²Service of Wood Biology, Royal Museum for Central Africa, Tervuren, Belgium

⁸³Department of Silviculture, Transilvania University of Brasov, Brasov, Romania

⁸⁴Wageningen University and Research, Wageningen Environmental Research (Alterra), Wageningen, the Netherlands

⁸⁵Disturbance Ecology, Bayreuth Center of Ecology and Environmental Research, University of Bayreuth, Bayreuth, Germany

⁸⁶Botanical Garden, University of Wrocław, Wrocław, Poland

⁸⁷Norwegian Institute of Bioeconomy Research, Tromsø, Norway

⁸⁸Biodiversity and Conservation Biology, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland

⁸⁹Faculty of Forestry, Kilavuzlar Köyü Öte Karsi Üniversitesi Kampüsü Merkez, Karabük University, Karabük, Turkey

⁹⁰Department Environment, Computational and Applied Vegetation Ecology (UGent-CAVELab), Ghent University, Ghent, Belgium

⁹¹Department of Systematic and Evolutionary Botany, University of Zurich, Zurich, Switzerland

⁹²Branch of the M.V. Keldysh Institute of Applied Mathematics of Russian Academy of Sciences, Institute of Mathematical Problems of Biology of RAS, Pushchino, Russian Federation

⁹³Museo de Historia Natural Noel Kempff Mercado, Universidad Autonoma Gabriel Rene Moreno, Santa Cruz de la Sierra, Bolivia

⁹⁴Geosystem Laboratory, Central Siberian Botanical Garden, Siberian Branch, Russian Academy of Sciences, Novosibirsk, Russian Federation

⁹⁵Department of Biodiversity, Macroecology and Biogeography, University of Göttingen, Göttingen, Germany

⁹⁶Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany

⁹⁷Department of Geobotany and Ecology, M.G. Kholodny Institute of Botany of the National Academy of Sciences of Ukraine, Kyiv, Ukraine

⁹⁸Centre for Ecological Research, Institute of Ecology and Botany, Vácrátót, Hungary

⁹⁹Research Group Functional Traits, Naturalis Biodiversity Center, Leiden, the Netherlands

¹⁰⁰Institute of Biology Leiden, Leiden University, Leiden, the Netherlands

¹⁰¹Departamento de Engenharia Florestal, Universidade Regional de Blumenau, Blumenau, Brazil

¹⁰²College of Urban and Environmental Sciences, Peking University, Beijing, China

¹⁰³Laboratory of Vegetation Science, Komarov Botanical Institute RAS, Saint-Petersburg, Russian Federation

¹⁰⁴Laboratory of Phytodiversity Problems, Institute of Ecology of the Volga River Basin RAS - Branch of the Samara Scientific Center RAS, Togliatti, Russian Federation

¹⁰⁵Group of Ecology of Living Organisms, Tobolsk complex scientific station of Ural Branch RAS, Tobolsk, Russian Federation

¹⁰⁶Remote Sensing Centre for Earth System Research, University of Leipzig, Leipzig, Germany

¹⁰⁷Ufa Institute of Biology, Ufa Federal Scientific Center of the Russian Academy of Sciences, Ufa, Russian Federation

¹⁰⁸Department of Bioscience, Aarhus University, Roende, Denmark

¹⁰⁹Jardín Botánico de Missouri Oxapampa, Oxapampa, Pasco, Peru

¹¹⁰Universidad Nacional de San Antonio Abad del Cusco, Cusco, Peru

¹¹¹Murdoch University, Murdoch, Perth, Western Australia, Australia

¹¹²Department of Geography & Environmental Studies, Stellenbosch University, Stellenbosch, South Africa

¹¹³Conservation Science, Royal Botanic Gardens, Kew, Ardingly, UK

¹¹⁴CIRAD, CNRS, INRAE, AMAP, Université de Montpellier, Montpellier, France

¹¹⁵Department of Plant Biology, University of Mazandaran, Mazandaran, Iran

¹¹⁶Department of Botany and Biodiversity Research, University of Vienna, Vienna, Austria

¹¹⁷Botanical Garden - Center for Biodiversity Conservation, Polish Academy of Sciences, Warsaw, Poland

¹¹⁸Institute of Biology, University of Opole, Opole, Poland

¹¹⁹National Academy of Sciences of Ukraine, M.G. Kholodny Institute of Botany, Kyiv, Ukraine

¹²⁰Department of Botany, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil

¹²¹Laboratorio de Invasiones Biológicas (LIB), Facultad de Ciencias Forestales, Universidad de Concepción, Concepción, Chile

¹²²Institute of Ecology and Biodiversity (IEB), Santiago, Chile

¹²³Department of Biology, University of North Carolina, Chapel Hill, North Carolina, USA

¹²⁴Global Ecology Unit CSIC-CREAF-UAB, CSIC, Bellaterra, Catalonia, Spain

¹²⁵CREAF, Cerdanyola del Valles, Spain

¹²⁶Department of Biosciences, University of Vic-Central University of Catalonia, Barcelona, Spain

¹²⁷Department of Evolutionary Biology, Ecology and Environmental Sciences, University of Barcelona, Barcelona, Spain

¹²⁸Department of Vegetation Ecology, Institute of Botany, Czech Academy of Sciences, Průhonice, Czech Republic
¹²⁹Department of Civil and Environmental Engineering, University of the Andes, Bogota, Colombia

¹³⁰Institute of Environmental Sciences, Kazan Federal University, Kazan, Russian Federation

¹³¹Institute of Botany, Nature Research Centre, Vilnius, Lithuania

¹³²Biodiversity, Ecology and Evolution of Plants/Institute for Plant Science & Microbiology, University of Hamburg, Hamburg, Germany

¹³³Faculty of Natural Resources and Spatial Sciences, Namibia University of Science and Technology, Windhoek, Namibia

¹³⁴Universidad San Francisco de Quito, COCIBA, Quito, Ecuador

¹³⁵Independent Researcher, Lancaster, UK

¹³⁶Hungarian Department of Biology and Ecology, Faculty of Biology and Geology, Babeș-Bolyai University, Cluj-Napoca, Romania

¹³⁷Faculty of Geography and Earth Sciences, University of Latvia, Riga, Latvia

¹³⁸Climatology, Bayreuth Center of Ecology and Environmental Research (BayCEER), University of Bayreuth, Bayreuth, Germany

¹³⁹Palmengarten, Stadt Frankfurt am Main - Der Magistrat, Frankfurt am Main, Germany

¹⁴⁰Microsoft, Redmond, Washington, USA

¹⁴¹Institute of Botany, Plant Science and Biodiversity Centre Slovak Academy of Sciences, Bratislava, Slovakia

¹⁴²Institute of Biology, Research Centre of Slovenian Academy of Sciences and Arts (ZRC SAZU), Ljubljana, Slovenia

¹⁴³Department of Botany, Charles University, Prague, Czech Republic

¹⁴⁴Faculty of Forestry and Wood Technology, University of Zagreb, Zagreb, Croatia

¹⁴⁵TERN, University of Adelaide, Adelaide, South Australia, Australia

¹⁴⁶CSIC-UV-GV, Centro de Investigaciones sobre Desertificación, Moncada, Spain

¹⁴⁷Faculty of Geotechnical Engineering, University of Zagreb, Varaždin, Croatia

¹⁴⁸Department of Biology, Aarhus University, Aarhus C, Denmark

¹⁴⁹School of Ecology and Environmental Science, Yunnan University, Chenggong New District, Kunming, China

¹⁵⁰School of Biology, Aristotle University of Thessaloniki, Thessaloniki, Greece

¹⁵¹Department of Geography, University of Erlangen-Nuremberg, Erlangen, Germany

¹⁵²ILEX Consultoria Científica, Porto Alegre, Brazil

¹⁵³Department of Chemistry, Biology and Biotechnology, University of Perugia, Perugia, Italy

¹⁵⁴Univ Montpellier, CNRS, EPHE, IRD, Univ Paul Valéry Montpellier 3, CEFE, Montpellier, France

¹⁵⁵Ecology and Genetics Research Unit, Biodiversity Unit, University of Oulu, Oulu, Finland

¹⁵⁶Department of Physiological Diversity, Helmholtz Center for Environmental Research - UFZ, Leipzig, Germany

¹⁵⁷Institute of Ecology, Leuphana University of Lüneburg, Lüneburg, Germany

¹⁵⁸Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada

¹⁵⁹Institute of Arctic Biology, University of Alaska, Fairbanks, Alaska, USA

¹⁶⁰Botany, University of Wisconsin-Madison, Madison, Wisconsin, USA

¹⁶¹Hainan Key Laboratory for Sustainable Utilization of Tropical Bioresources, College of Tropical Crops, Hainan University, Haikou, China

¹⁶²Botany Department, Senckenberg Museum of Natural History Görlitz, Görlitz, Germany

¹⁶³International Institute Zittau, Technische Universität Dresden, Zittau, Germany

¹⁶⁴Bell Museum, University of Minnesota, St. Paul, Minnesota, USA

¹⁶⁵Forest Dynamics, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland

¹⁶⁶Laboratory of Wild-Growing Flora, South-Ural Botanical Garden-Institute, Ufa Scientific Centre, Russian Academy of Sciences, Ufa, Russian Federation

Correspondence

Francesco Maria Sabatini, German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstraße 4, 04103, Leipzig, Germany.
Email: francesco.sabatini@botanik.uni-halle.de
## Funding information

Agence Nationale de la Recherche, Grant/
Award Number: ANR-07-BDIV-0006, ANR-
07-BDIV-0008 and ANR-07-BDIV-0010;
H2020 European Research Council,
Grant/Award Number: ERC Advanced
Grant 291585 "T-FORCES" and ERC-SyG-
2013-610028 IMBALANCE-P; Villum
Fonden, Grant/Award Number: 16549;
Deutsche Forschungsgemeinschaft, Grant/
Award Number: DFG FZT 118, 202548816,
DFG Ho3296-2, DFG Ho3296-4, DFG
VA 749/1-1, DFG VA 749/4-1 and DFG
WE 2601/3-1 3-2 4-1 4-2; Narodowe
Centrum Nauki, Grant/Award Number:
2017/25/B/NZ8/00572; Latvia grant,
Grant/Award Number: AAP2016/B041//
Zd2016/AZ03; NSF, Grant/Award Number:
DEB-0415383; Horizon 2020 Framework
Programme, Grant/Award Number:
640176; U.S. National Science Foundation,
Grant/Award Number: DBI-0213794
and DBI-9905838; Grantová Agentura
České Republiky, Grant/Award Number:
19-28491X; German Centre for Integrative
Biodiversity Research, Grant/Award
Number: 50170649_#7; Fundación BBVA,
Grant/Award Number: BIOCON08_044;
Akademie Věd České Republiky, Grant/
Award Number: RVO 67985939; Spanish
Research Agency, Grant/Award Number:
AEI/10.13039/501100011033; National
Research, Development and Innovation
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PD-12399; Schweizerischer Nationalfonds
zur Förderung der Wissenschaftlichen
Forschung, Grant/Award Number:
20BD21_184131; Basque Government,
Grant/Award Number: 640176; Russian
Foundation for Basic Research, Grant/Award
Number: 16-04-00747a; Brazil's National
Council of Scientific and Technological
Development, Grant/Award Number:
307689/2014-0 and 310022/2015-0;
Volkswagen Foundation, Grant/Award
Number: AZ I/81 976

Handling Editor: Amanda Bates

## Abstract

**Motivation:** Assessing biodiversity status and trends in plant communities is critical for understanding, quantifying and predicting the effects of global change on ecosystems. Vegetation plots record the occurrence or abundance of all plant species co-occurring within delimited local areas. This allows species absences to be inferred, information seldom provided by existing global plant datasets. Although many vegetation plots have been recorded, most are not available to the global research community. A recent initiative, called 'sPlot', compiled the first global vegetation plot database, and continues to grow and curate it. The sPlot database, however, is extremely unbalanced spatially and environmentally, and is not open-access. Here, we address both these issues by (a) resampling the vegetation plots using several environmental variables as sampling strata and (b) securing permission from data holders of 105 local-to-regional datasets to openly release data. We thus present sPlotOpen, the largest open-access dataset of vegetation plots ever released. sPlotOpen can be used to explore global diversity at the plant community level, as ground truth data in remote sensing applications, or as a baseline for biodiversity monitoring.

**Main types of variable contained:** Vegetation plots (n = 95,104) recording cover or abundance of naturally co-occurring vascular plant species within delimited areas. sPlotOpen contains three partially overlapping resampled datasets (c. 50,000 plots each), to be used as replicates in global analyses. Besides geographical location, date, plot size, biome, elevation, slope, aspect, vegetation type, naturalness, coverage of various vegetation layers, and source dataset, plot-level data also include community-weighted means and variances of 18 plant functional traits from the TRY Plant Trait Database.

**Spatial location and grain:** Global, 0.01–40,000 m².

**Time period and grain:** 1888–2015, recording dates.

**Major taxa and level of measurement:** 42,677 vascular plant taxa, plot-level records.

**Software format:** Three main matrices (.csv), relationally linked.

## KEYWORDS

big data, biodiversity, biogeography, database, functional traits, macroecology, vascular plants, vegetation plots

# 1 | BACKGROUND & SUMMARY

Biodiversity is facing a global crisis. As many as 1 million species are currently threatened with extinction, the vast majority due to anthropogenic impacts such as land-use and climate change (IPBES, 2019; WWF, 2020). In addition, the rates of biodiversity homogenization and redistribution are accelerating (Fricke & Svenning, 2020; Lenoir et al., 2020; Staude et al., 2020). Biological assemblages are becoming progressively more similar to each other globally, as local and endemic species go extinct and are replaced by more widespread and competitive native or alien species (IPBES, 2019; Staude et al., 2020). Many terrestrial and marine species are also shifting their geographical distribution as a response to climate change

(Lenoir et al., 2020). This has profound potential impacts on ecosystems and human health (Bonebrake et al., 2018; Pecl et al., 2017).

Plant communities are no exception to this biodiversity crisis (Cardinale et al., 2011; Lenoir et al., 2008; Staude et al., 2020). This is particularly worrying since terrestrial vegetation accounts for 80% (450 Gt C) of the living biomass on Earth (Bar-On et al., 2018). Given the central role of vegetation in ecosystem productivity, structure, stability and functioning (Cardinale et al., 2011), assessing biodiversity status and trends in plant communities is paramount for other kingdoms of life and human societies alike.

Monitoring trends in plant biodiversity requires adequate data across a range of spatio-temporal scales (Kühl et al., 2020; Pimm, 2021). Large independent collections of plant occurrence
data do exist at the global or continental extent via the Botanical
Information and Ecology Network (BIEN; Enquist et al., 2016), the
Global Inventory of Floras and Traits (GIFT; Weigelt et al., 2020) or
the Global Biodiversity Information Facility (GBIF; https://www.gbif.
org/). However, these databases suffer from one or several of the
following limitations: (a) imbalance towards tree species only; (b) lack
of data on how individual plant species co-occur and interact locally
to form plant communities; and (c) coarse spatial resolutions (e.g.,
one-degree grid cells), which preclude intersection with high resolu-
tion remote sensing data and the assessment of biodiversity trends
at the plant community level (Boakes et al., 2010).

There is a long tradition among botanists and phytosociologists
to record the cover or abundance of each plant species that occurs
in a vegetation plot (here used as a synonym of 'relevé' or 'quad-
rat') of a given size (i.e., surface area) at a given time (e.g., Stebler &
Schröter, 1892). Compared to presence-only data, vegetation-plot
data present many advantages. As all visible plant species are re-
corded, plots contain information on which plant species do, and do
not co-occur in the same locality at a given moment in time (Chytrý
et al., 2016). This is important for testing hypotheses related to bi-
otic interactions among plant species. Vegetation-plot data also pro-
vide crucial information on where and when a species was absent,
therefore, improving predictions from current species distribution
models (Phillips et al., 2009). Being spatially explicit, vegetation
plots can be resurveyed through time to assess potential changes in
plant species composition relative to a baseline (Perring et al., 2018;
Staude et al., 2020; Steinbauer et al., 2018). As they normally con-
tain information on the relative cover or abundance of each species,
vegetation plots are also more appropriate for detecting biodiversity
changes than data representing only the occurrence of individual
species (Beck et al., 2018; Jandt et al., 2011).

Globally, however, vegetation-plot data are very fragmented,
as they typically stem from a myriad of local research and sur-
vey projects (Bruelheide et al., 2019). These are fine-grained data
(e.g., 1-10,000 m²) normally covering small spatial extents (e.g., 1-
1,000 km²). With their disparate sampling protocols, standards and
taxonomic resolutions, aggregating and harmonizing vegetation plot
data proves extremely challenging (Bruelheide et al., 2018). It is not
surprising, therefore, that these data are rarely used in global-scale
research on the biodiversity of plant communities (Aubin et al., 2020;
Franklin et al., 2017; Wiser, 2016).

The sPlot initiative tries to close this data gap. It consolidates
numerous local to regional vegetation-plot datasets to create a
harmonized and comprehensive global database of georeferenced
terrestrial plant species assemblages (Bruelheide et al., 2019).
Established in 2013, sPlot v3.0 currently contains more than 1.9 mil-
lion vegetation plots, and is fully integrated with the TRY database
(Kattge et al., 2020), from which it derives information on plant func-
tional traits. The sPlot database is increasingly being used to study
continental-to-global scale vegetation patterns (Cai et al., 2021;
Testolin, Attorre, et al., 2021; Testolin, Carmona, et al., 2021),
such as the relative contribution of regional versus local factors
to the global patterns of fern richness (Weigand et al., 2020), the

mechanisms underlying the spread and abundance of native versus
invasive tree species (van der Sande et al., 2020), and worldwide
trait-environment relationships in plant communities (Bruelheide
et al., 2018).

Yet, most of these data are not open-access. Here, we secured
permission from data holders in the sPlot database to openly release
a dataset composed of 95,104 vegetation plots. We selected the
plots to be released using a replicated environmental stratification,
in order to represent the entire environmental space covered by the
sPlot database. This maximizes the benefits of releasing these data
for a wide range of potential uses. The selected vegetation plots
stem from 105 databases and span 114 countries (Figure 1). This
resampled dataset (sPlotOpen - hereafter) is composed of: (a) plot-
level information, including metadata and basic vegetation structure
descriptors; (b) the vascular plant species composition of each vege-
tation plot, including species cover or abundance information when
available; and (c) community-level functional information obtained
by intersection with the TRY database (Kattge et al., 2020).

sPlotOpen is specifically designed for global macroecological
studies, for example, the exploration of functional diversity patterns
of communities with continental-to-global extent. We expect, how-
ever, that sPlotOpen might likewise prove useful to answer a range
of different questions, related for instance to species co-occurrence
patterns, the definition of species pools, the link between regional
versus local determinants of species diversity, or the niche overlap
between co-occurring species. Yet, data in sPlotOpen should not be
considered as representative of the distribution of plant communi-
ties worldwide, especially when working at local spatial extents. This
should be kept in mind for applications such as species distribution
models (SDMs) or joint SDMs, whose results might be affected by
the uneven geographical distribution of sPlotOpen's data. We refer
the reader to the section 'Usage notes' for additional guidance on
critical issues related, for instance, to incompletely sampled vegeta-
tion plots, varying plot size, and nested vegetation plots.

2 | METHODS

2.1 | Vegetation plot data sources

We started from the sPlot database v2.1 (created in October 2016),
which contains 1,121,244 unique vegetation plots and 23,586,216
species records. Most of the data in sPlot refer to natural and semi-
natural vegetation, while vegetation shaped by intensive and re-
peated human interference, such as cropland or ruderal communities,
is hardly represented. Data originate from 110 different vegetation-
plot datasets of regional, national or continental extent, some of
which stem from regional or continental initiatives (see Bruelheide
et al., 2019, for more information). For instance: 48 vegetation-
plot datasets derive from the European Vegetation Archive (EVA;
Chytrý et al., 2016); three major African datasets derive from the
Tropical African Vegetation Archive (TAVA); and multiple vegetation
datasets in the USA and Australia derive from the VegBank (Peet,
<figure><img src="image_4.png" /><figcaption>**FIGURE 1** Top: global distribution of all vegetation plots contained in sPlotOpen (n = 95,104). Each colour represents a different source dataset (n = 105 - different datasets might have the same colour). Bottom: spatial distribution of vegetation plot density for the environmentally balanced dataset selected by the first resampling iteration (n = 49,787). Densities are calculated in hexagonal cells with a spatial resolution of approximately 70,000 km². Map projection is Eckert IV [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

Lee, Boyle, et al., 2012; Peet, Lee, Jennings, et al., 2012) and TERN's AEKOS (Chabbi & Loescher, 2017) archives, respectively. Data from other continents (South America, Asia) or countries were contributed as separate standalone datasets. The metadata of each individual vegetation-plot dataset stored in sPlot are managed through the Global Index of Vegetation-Plot Databases (GIVD; Dengler et al., 2011), using the GIVD code as the unique dataset identifier.

## 2.2 | Resampling method

Data in the sPlot database are unevenly distributed across vegetation types and geographical regions (see Bruelheide et al., 2018).

Mid-latitude regions in developed countries (mostly Europe, the USA and Australia) are overrepresented in sPlot, while regions in the tropics and subtropics are underrepresented, which is a typical geographical bias in biodiversity data (see Lenoir et al., 2020; Lenoir & Svenning, 2015 for similar geographical bias in species redistribution). Such a geographical bias usually translates into an environmental bias with temperate climate usually more represented than tropical or Mediterranean climates. Unbalanced sampling effort in the environmental space is of particular concern for comparative macroecological studies (Bruelheide et al., 2018; Lenoir et al., 2010). To reduce this imbalance as much as possible, we performed a stratified resampling approach within the environmental space using several environmental variables available at global extent as sampling strata.
First, we removed vegetation plots without geographical coordinates or with a location uncertainty higher than 3 km. We also removed vegetation plots identified by the respective data contributors as having been recorded in wetlands or in anthropogenic vegetation types, since these data were available only for a few geographical regions, mostly in Europe. This resulted in a total of 799,400 out of the initial set of 1,121,244 vegetation plots.

We then ran a global principal component analysis (PCA) on a matrix of all terrestrial grid cells at a spatial resolution of 2.5 arcmin (n = 8,384,404), based on 30 climatic and soil variables. For climate, we used the 19 bioclimatic variables from CHELSA (Climatologies at high resolution for the earth's land surface areas) v1.2 (Karger et al., 2017), as well as two other bioclimatic variables reflecting the growing-season length (growing degree days above 1 °C – GDD1 – and 5 °C – GDD5), which were derived from CHELSA's monthly temperatures as in Synes and Osborne (2011). In addition, we considered an index of aridity and a layer for potential evapotranspiration from the Consortium of Spatial Information (CGIAR-CSI, Trabucco & Zomer, 2010). For soil, we extracted seven variables from the SoilGrids database (Hengl et al., 2017), namely: (a) soil organic carbon

content in the fine earth fraction; (b) cation exchange capacity; (c) pH; as well as the fractions of (d) coarse fragments; (e) sand; (f) silt; and (g) clay. The results of this PCA represent the full environmental space of all terrestrial habitats on Earth, irrespective of whether a grid cell hosted vegetation plots or not (Supporting Information Figure S1). We then subdivided the PCA ordination space, represented by the first two principal components (PC1-PC2), which accounted for 47 and 23%, respectively, of the total environmental variation in terrestrial grid cells, into a regular 100 × 100 grid. This PC1-PC2 two-dimensional space was subsequently used to balance our sampling effort across all PC1-PC2 grid cells for which vegetation plots were available. After excluding 42,878 vegetation plots for which no PC1 or PC2 values were available, due to missing data in the bioclimatic or soil variables, we projected the remaining 756,522 vegetation plots onto this PC1-PC2 grid. We finally calculated how many vegetation plots occurred in each PC1-PC2 grid cell (Figure 2).

In total, vegetation plots were available for 1,720 out of the 4,125 PC1-PC2 grid cells covered by the 8,384,404 terrestrial grid cells of the geographical space. We then resampled those PC1-PC2 grid cells (n = 858) with more than 50 vegetation plots, which is

<figure><img src="image_5.png" /></figure>

**FIGURE 2** Distribution of vegetation plots from sPlotOpen in the global environmental space based on a principal component analysis (PCA) using 30 climate and soil variables. Top: spatial distribution of PCA values across all terrestrial grid cells (n = 8,384,404, spatial grain = 2.5 arcmin). Bottom left: distribution of plots compared to the distribution of all terrestrial 2.5 arc-minute cells (grey background) in the PCA space. Only the plots in the environmentally balanced dataset selected in the first resampling iteration are shown (n = 49,787). The PCA space was divided into a 100 × 100 regular grid. The first and second PCA axes explained 47 and 23% of the total variance, respectively. Bottom right: geographical distribution of the vegetation plots contained in four randomly selected PCA grid cells [Colour figure can be viewed at wileyonlinelibrary.com]
the median number of plots occurring across occupied grid cells in sPlot. This threshold of 50 vegetation plots represents a compromise between selecting a high number of plots, and keeping the resampled dataset as balanced as possible across the PC1-PC2 environmental space. To select these 50 vegetation plots we used the heterogeneity-constrained random resampling algorithm (Lengyel et al., 2011). This algorithm quantifies the variability in plant species composition among a set of vegetation plots by computing the mean and the variance of the Jaccard's dissimilarity index (Jaccard, 1912) between all possible pairs of vegetation plots. More precisely, for a given PC1-PC2 grid cell containing more than 50 vegetation plots, we generated 1,000 random selections of 50 vegetation plots and ranked each selection according to the mean (ascending order) and variance (descending order) value of the Jaccard's dissimilarity index. Ranks from both sortings were summed for each random selection, and the selection with the lowest summed rank was considered to provide the most balanced/even representation of vegetation types within the focal grid cell. Where a grid cell contained fewer than 50 plots, we retained all of them. In this way, we reduced the imbalance towards over-sampled climate types while ensuring that the resampled dataset represents the entire environmental gradient covered by the original sPlot database. This approach optimizes the selection of a subset of vegetation plots that encompasses the highest variability in species composition while avoiding peculiar and rare communities, which may represent outliers. As such, our approach maximizes variability over representativeness within each grid cell. We repeated the whole resampling procedure three times to get three different environmentally balanced, resampled subsets of our vegetation plots. These three resampling iterations can therefore be used as separate replicates, albeit these are not completely independent, as the same plots might have been drawn in two or even three of the three resampling iterations. In addition, those plots located in PC1-PC2 grid cells with fewer than 50 vegetation plots are completely shared by all three iterations.

## 2.3 | Permission to release the data as open access

The resampling procedure resulted in 56,486, 56,501 and 56,494 vegetation plots selected during resampling iterations #1, #2 and #3, respectively, for a total of 107,238 unique vegetation plots. Since the sPlot database is a consortium of independent datasets whose copyright belongs to the data contributors, we used this preliminary potential selection to ask each dataset's custodian (i.e., either the owner of a dataset or its authorized representative in the case of a collective dataset) for permission to release the data of selected vegetation plots as open access. For 12,134 unique vegetation plots, permission could not be granted because, for instance, the data are unpublished, confidential or sensitive. The number of vegetation plots for which the open-access permission was not granted in resampling iterations #1, #2 and #3 was 6,699, 6,690 and 6,705, respectively.

To mitigate the imbalance due to the exclusion of these confidential plots, we created a 'consensus' dataset. We started from resampling iteration #1, and replaced the 6,699 plots not granted as open access with plots selected in the second and third iterations, for which such permission could be granted ('reserve' plots, hereafter). We imposed the constraint that each candidate vegetation plot in the reserve pool should belong to the same environmental stratum, that is, the same PC1-PC2 grid cell, as the confidential vegetation plot, even though we acknowledge that this procedure does not maximize the variability in plant species composition of the replacement plots. Even after drawing from reserves, there were 3,150 plots that could not be replaced. These were distributed across 279 PC1-PC2 grid cells (16.2% of occupied cells), each cell having on average 11 irreplaceable plots (min. = 1, median = 5, max. = 50).

## 2.4 | Trait information

For each vegetation plot for which open access could be granted, we computed the community-weighted mean and variance for 18 plant functional traits derived from the TRY database v3.0 (Kattge et al., 2020). These traits were selected among those that describe the leaf, wood, and seed economics spectra (Reich, 2014; Westoby, 1998), and are known to either affect different key ecosystem processes or respond to macroclimatic drivers, or both (Bruelheide et al., 2018). The 18 plant functional traits (all concentrations based on dry weight) were: (a) leaf area (mm²); (b) stem specific density (g/cm³); (c) specific leaf area (m²/kg); (d) leaf carbon concentration (mg/g); (e) leaf nitrogen concentration (mg/g); (f) leaf phosphorus concentration (mg/g); (g) plant height (m); (h) seed mass (mg); (i) seed length (mm); (j) leaf dry matter content (g/g); (k) leaf nitrogen per area (g/m²); (l) leaf N:P ratio (g/g); (m) leaf δ¹⁵N (per million); (n) seed number per reproductive unit; (o) leaf fresh mass (g); (p) stem conduit density (per mm²); (q) dispersal unit length (mm); and (r) conduit element length (µm).

Because missing values were particularly widespread in the species-trait matrix, we calculated community-weighted means using the gap-filled version of these traits we received from TRY (Kattge et al., 2020). Gap-filling was performed at the level of individual observations and relies on hierarchical Bayesian modelling (R package 'BHPMF' - Fazayeli et al., 2014; Schrodt et al., 2015) in R (R Core Team, 2020). This is a Bayesian machine learning approach, with no a priori assumptions, except for the data being missing completely at random. The algorithm 'learns' from the data, that is, if there was a phylogenetic signal in the data, this was used to fill the gaps but where no such signal was apparent, none was introduced. After gap-filling, we transformed to the natural logarithm all gap-filled trait values and averaged each trait by taxon (i.e., at species or genus level). The gap-filling approach was run only for species having at least one trait observation (n = 21,854). Additional information on the gap-filling procedure is available in Bruelheide et al. (2019).
Community-weighted means (CWM) and variances (CWV) were calculated for every plant functional trait j and every vegetation plot k as follows (Enquist et al., 2015):

$$CWM_{j,k} = \sum_{i}^{n_k} p_{i,k} t_{i,j} \qquad (1)$$

$$CWV_{j,k} = \sum_{i}^{n_k} p_{i,k} (t_{i,j} - CWM_{j,k})^2 \qquad (2)$$

where $n_k$ is the number of species with trait information in vegetation plot k, $p_{i,k}$ is the relative abundance of species i in vegetation plot k calculated as the species' fraction in cover or abundance of total cover or abundance, and $t_{i,j}$ is the mean value of species i for trait j.

## 3 | DATA RECORDS

sPlotOpen contains 95,104 unique vegetation plots from 105 constitutive datasets (Table 1) and from 114 countries covering all continents except Antarctica (Figure 1). This is the result of pooling together the three environmentally balanced datasets from resampling iterations #1, #2 and #3 containing 49,787, 49,811 and 49,789 plots, respectively, after excluding the set of plots for which open access could not be granted by data contributors. The number of plots shared across all three resampling iterations is 19,672, while 14,939 plots are shared between two iterations. Replacing confidential plots in resampling iteration #1 with reserves from the other two iterations in the same PC1-PC2 grid cell resulted in a consensus version containing 53,262 plots. sPlotOpen only contains the species composition of vascular plants; information on the composition of bryophytes and lichens was discarded since it was only available for a minority of plots (n = 11,001 and n = 6,801, respectively). Information on the size (surface area) of the vegetation survey is available for 67,022 plots, and ranges between 0.03 and 40,000 m² (mean = 377 m²; median = 100 m²). Specifically, sPlotOpen contains 12,894 plots with size smaller than 10 m², 25,742 with size 10-100 m², 24,750 plots with size 100-1,000 m² and 3,075 plots with size greater or equal to 1,000 m². Similarly, only for a minority of plots (n = 24,167) is information on the exact group of plants sampled in the field available (e.g., complete vegetation, only trees, only trees > 1 m height, and so on). However, as most data were collected using the phytosociological method, we deem it safe to assume that, unless otherwise specified, plots contain information on all vascular plants. We retained plots with incomplete vegetation, because they were mostly located in the tropics, that is, in areas where vegetation plots are particularly scarce otherwise. The average number of vascular plant species per vegetation plot ranges between 1 (i.e., monospecific stands) and 271 species (mean = 20; median = 16).

By capping the number of vegetation plots in overrepresented environmental conditions, the resampling procedure described above strongly reduced the bias in the distribution of vegetation plots within the PC1-PC2 environmental space. Yet, due to the lack

or scarcity of data from some geographical regions, like the tropics, there is some remaining imbalance in the spatial distribution of vegetation plots across geographical regions (Figure 1). This is evident when comparing the number of plots across continents. When considering the first resampling iteration only (n = 49,787), Europe is by far the best represented continent, with 15,920 vegetation plots. The least represented continents are Africa and South America, with 3,709 and 5,498 vegetation plots, respectively. Some residual imbalance remains also when considering biomes (Figure 3). With the exception of the 'Temperate mid-latitudes' biome, which includes 14,100 vegetation plots, all other biomes have a number of plots comprised between 1,558 ('Polar and subpolar zone') and 6,245 ('Subtropics with year-round rain') vegetation plots (Figure 3, left). Despite this residual imbalance, all the Whittaker biomes are covered by sPlotOpen (Figure 3, right), and our resampling algorithm has resulted in a much more balanced dataset than many other global datasets that are available, such as GBIF.

Almost one third of the 95,104 vegetation plots in sPlotOpen belong to forests (n = 38,282), one half to non-forest vegetation (n = 45,735), with 11.6% of plots remaining unassigned (n = 11,087). When not directly done by data providers, the assignment of plots to forests and non-forests was based on multiple lines of evidence, including the plot-level information on the cover of the tree layer, as well as traits of species composing a plot, such as growth form and height. In short, a plot record was considered as forest if the cover of the tree layer, or alternatively, the sum of the (relative) cover of all tree taxa (scaled by the sum of all cover values, as a percentage), was greater than 25%. It was considered a non-forest record if the sum of relative cover of low-stature, non-tree and non-shrub taxa was greater than 90%. For an extensive explanation of this classification scheme, we refer the reader to Bruelheide et al. (2019). Even though the proportion of forest versus non-forest vegetation plots is relatively well balanced, the geographical distribution of vegetation plots belonging to different vegetation types is likely not balanced in the geographical space, as it depends on the idiosyncrasies of the constitutive datasets composing the sPlot database. For instance, the data from New Zealand only include plots collected in non-forest ecosystems, while data from Chile only refer to forests. We urge potential users to carefully read the section 'Usage notes' below and the description of each individual dataset in GIVD (Dengler et al., 2011), and to contact the custodians of each dataset for further information.

## 4 | DATABASE ORGANIZATION

The environmentally balanced and open-access dataset sPlotOpen is organized into three main matrices, relationally linked through the key column 'PlotObservationID'.

The 'header' matrix contains plot-level information for the 95,104 vegetation plots, including: metadata (e.g., plot ID, data source, sampling date, geographical location, positional accuracy); sampling design information (e.g., the total surface area used during the vegetation survey); and a plot-level description of vegetation
<table><caption>TABLE 1 List of datasets contributing to sPlotOpen, the environmentally balanced and open-access database of vegetation plots</caption><thead><tr><td>GIVD ID</td><td>Dataset name</td><td>Custodian</td><td>Deputy custodian</td><td>No. open-access plots</td><td>Reference</td></tr></thead><tbody><tr><td>00-00-001</td><td>ForestPlots.net</td><td>Oliver L. Phillips</td><td>Aurora Levesley</td><td>169</td><td>Lopez-Gonzalez et al. (2011)</td></tr><tr><td>00-00-003</td><td>SALVIAS</td><td>Brian Enquist</td><td>Brad Boyle</td><td>3,403</td><td></td></tr><tr><td>00-00-004</td><td>Vegetation Database of Eurasian Tundra</td><td>Risto Virtanen</td><td></td><td>519</td><td></td></tr><tr><td>00-00-005</td><td>Tundra Vegetation Plots (TundraPlot)</td><td>Anne D. Bjorkman</td><td>Sarah Elmendorf</td><td>309</td><td>Elmendorf et al. (2012)</td></tr><tr><td>00-RU-001</td><td>Vegetation Database Forest of Southern Ural</td><td>Vasiliy Martynenko</td><td>Pavel Shirokikh</td><td>68</td><td></td></tr><tr><td>00-RU-002</td><td>Database of Masaryk University's Vegetation Research in Siberia</td><td>Milan Chytrý</td><td></td><td>158</td><td>Chytrý (2012)</td></tr><tr><td>00-RU-003</td><td>Database Meadows and Steppes of Southern Ural</td><td>Sergey Yamalov</td><td>Mariya Lebedeva</td><td>238</td><td></td></tr><tr><td>00-TR-001</td><td>Forest Vegetation Database of Turkey - FVDT</td><td>Ali Kavgacı</td><td></td><td>45</td><td></td></tr><tr><td>AF-00-001</td><td>West African Vegetation Database</td><td>Marco Schmidt</td><td>Georg Zizka</td><td>258</td><td>Schmidt et al. (2012)</td></tr><tr><td>AF-00-003</td><td>BIOTA Southern Africa Biodiversity Observatories Vegetation Database</td><td>Norbert Jürgens</td><td>Ute Schmiedel</td><td>1,015</td><td>Muche et al. (2012)</td></tr><tr><td>AF-00-006</td><td>SWEA-Dataveg</td><td>Miguel Alvarez</td><td>Michael Curran</td><td>1,675</td><td>Alvarez et al. (2021)</td></tr><tr><td>AF-00-008</td><td>PANAF Vegetation Database</td><td>Hjalmar S. Kühl</td><td>TeneKwetche Sop</td><td>884</td><td></td></tr><tr><td>AF-00-009</td><td>Vegetation Database of the Okavango Basin</td><td>Rasmus Revermann</td><td>Manfred Finckh</td><td>378</td><td>Revermann et al. (2016)</td></tr><tr><td>AF-BF-001</td><td>Sahel Vegetation Database</td><td>Jonas V. Müller</td><td>Marco Schmidt</td><td>556</td><td>Müller (2003)</td></tr><tr><td>AF-CD-001</td><td>Forest Database of Central Congo Basin</td><td>Kim Sarah Jacobsen</td><td>Hans Verbeeck</td><td>140</td><td>Kearsley et al. (2013)</td></tr><tr><td>AF-ET-001</td><td>Vegetation Database of Ethiopia</td><td>Desalegn Wana</td><td>Anke Jentsch</td><td>67</td><td>Wana & Beierkuhnlein (2011)</td></tr><tr><td>AF-MA-001</td><td>Vegetation Database of Southern Morocco</td><td>Manfred Finckh</td><td></td><td>621</td><td>Finckh (2012)</td></tr><tr><td>AF-ZW-001</td><td>Vegetation Database of Zimbabwe</td><td>Cyrus Samimi</td><td></td><td>31</td><td>Samimi (2003)</td></tr><tr><td>AS-00-001</td><td>Korean Forest Database</td><td>Tomáš Černý</td><td>Jiri Dolezal</td><td>1,039</td><td>Černý et al. (2015)</td></tr><tr><td>AS-00-003</td><td>Vegetation of Middle Asia</td><td>Arkadiusz Nowak</td><td>Marcin Nobis</td><td>314</td><td>Nowak et al. (2017)</td></tr><tr><td>AS-00-004</td><td>Rice Field Vegetation Database</td><td>Arkadiusz Nowak</td><td></td><td>32</td><td></td></tr><tr><td>AS-BD-001</td><td>Tropical Forest Dataset of Bangladesh</td><td>Mohammed A. S. Arfin Khan</td><td>Fahmida Sultana</td><td>87</td><td></td></tr><tr><td>AS-CN-001</td><td>China Forest-Steppe Ecotone Database</td><td>Hongyan Liu</td><td>Fengjun Zhao</td><td>117</td><td>Liu et al. (2000)</td></tr><tr><td>AS-CN-002</td><td>Tibet-PaDeMoS Grazing Transect</td><td>Karsten Wesche</td><td>Yun Jäschke</td><td>58</td><td>Wang et al. (2017)</td></tr><tr><td>AS-CN-003</td><td>Vegetation Database of the BEF China Project</td><td>Helge Bruelheide</td><td></td><td>24</td><td>Bruelheide et al. (2011)</td></tr><tr><td>AS-CN-004</td><td>Vegetation Database of the Northern Mountains in China</td><td>Zhiyao Tang</td><td></td><td>124</td><td></td></tr><tr><td>AS-EG-001</td><td>Vegetation Database of Sinai in Egypt</td><td>Mohamed Z. Hatim</td><td></td><td>143</td><td>Hatim (2012)</td></tr><tr><td>AS-ID-001</td><td>Sulawesi Vegetation Database</td><td>Michael Kessler</td><td></td><td>24</td><td></td></tr><tr><td>AS-IR-001</td><td>Vegetation Database of Iran</td><td>Jalil Noroozi</td><td>Parastoo Mahdavi</td><td>277</td><td></td></tr><tr><td>AS-KZ-001</td><td>Database of Meadow Vegetation in the NW Tien Shan Mountains</td><td>Viktoria Wagner</td><td></td><td>13</td><td>Wagner (2009)</td></tr><tr><td>AS-MN-001</td><td>Southern Gobi Protected Areas Database</td><td>Henrik von Wehrden</td><td>Karsten Wesche</td><td>1,032</td><td>von Wehrden et al. (2009)</td></tr><tr><td>AS-RU-001</td><td>Wetland Vegetation Database of Baikal Siberia (WETBS)</td><td>Victor Chepinoga</td><td></td><td>9</td><td>Chepinoga (2012)</td></tr></tbody></table>

(Continues)
<table><caption>TABLE 1 (Continued)</caption><thead><tr><td>GIVD ID</td><td>Dataset name</td><td>Custodian</td><td>Deputy custodian</td><td>No. open-access plots</td><td>Reference</td></tr></thead><tbody><tr><td>AS-RU-002</td><td>Database of Siberian Vegetation (DSV)</td><td>Andrey Korolyuk</td><td>Andrei Zverev</td><td>3,634</td><td>Korolyuk & Zverev (2012)</td></tr><tr><td>AS-RU-004</td><td>Database of the University of Münster - Biodiversity and Ecosystem Research Group's Vegetation Research in Western Siberia and Kazakhstan</td><td>Norbert Hölzel</td><td>Wanja Mathar</td><td>207</td><td></td></tr><tr><td>AS-SA-001</td><td>Vegetation Database of Saudi Arabia</td><td>Mohamed Abd El-Rouf Mousa El-Sheikh</td><td></td><td>711</td><td>El-Sheikh et al. (2017)</td></tr><tr><td>AS-TJ-001</td><td>Eastern Pamirs</td><td>Kim André Vanselow</td><td></td><td>221</td><td>Vanselow (2016)</td></tr><tr><td>AS-TW-001</td><td>National Vegetation Database of Taiwan</td><td>Ching-Feng Li</td><td>Chang-Fu Hsieh</td><td>912</td><td></td></tr><tr><td>AS-YE-001</td><td>Socotra Vegetation Database</td><td>Michele De Sanctis</td><td>Fabio Attorre</td><td>236</td><td>De Sanctis & Attorre (2012)</td></tr><tr><td>AU-AU-002</td><td>AEKOS</td><td>Ben Sparrow</td><td></td><td>10,976</td><td>Chabbi & Loescher (2017)</td></tr><tr><td>AU-NC-001</td><td>New Caledonian Plant Inventory and Permanent Plot Network (NC-PIPPN)</td><td>Jérôme Munzinger</td><td>Philippe Birnbaum</td><td>98</td><td>Ibanez et al. (2014)</td></tr><tr><td>AU-NZ-001</td><td>New Zealand National Vegetation Database</td><td>Susan K. Wiser</td><td></td><td>1,127</td><td>Wiser et al. (2001)</td></tr><tr><td>AU-PG-001</td><td>Forest Plots from Papua New Guinea</td><td>Timothy J. S. Whitfeld</td><td>George D. Weiblen</td><td>60</td><td>Whitfeld et al. (2014)</td></tr><tr><td>EU-00-002</td><td>Nordic-Baltic Grassland Vegetation Database (NBGVD)</td><td>Jürgen Dengler</td><td>Łukasz Kozub</td><td>54</td><td>Dengler & Rūsiņa (2012)</td></tr><tr><td>EU-00-011</td><td>Vegetation-Plot Database of the University of the Basque Country (BIOVEG)</td><td>Idoia Biurrun</td><td>Itziar García-Mijangos</td><td>2,142</td><td>Biurrun et al. (2012)</td></tr><tr><td>EU-00-013</td><td>Balkan Dry Grasslands Database</td><td>Kiril Vassilev</td><td>Armin Macanović</td><td>269</td><td>Vassilev et al. (2012)</td></tr><tr><td>EU-00-016</td><td>Mediterranean Ammophiletea Database</td><td>Corrado Marcenò</td><td>Borja Jiménez-Alfaro</td><td>783</td><td>Marcenò & Jiménez-Alfaro (2017)</td></tr><tr><td>EU-00-017</td><td>European Coastal Vegetation Database</td><td>John A. M. Janssen</td><td></td><td>356</td><td></td></tr><tr><td>EU-00-018</td><td>The Nordic Vegetation Database</td><td>Jonathan Lenoir</td><td>Jens-Christian Svenning</td><td>1,735</td><td>Lenoir et al. (2013)</td></tr><tr><td>EU-00-019</td><td>Balkan Vegetation Database</td><td>Kiril Vassilev</td><td>Hristo Pedashenko</td><td>484</td><td>Vassilev et al. (2016)</td></tr><tr><td>EU-00-020</td><td>WetVegEurope</td><td>Flavia Landucci</td><td></td><td>127</td><td>Landucci et al. (2015)</td></tr><tr><td>EU-00-022</td><td>European Mire Vegetation Database</td><td>Tomáš Peterka</td><td>Martin Jiroušek</td><td>2,560</td><td>Peterka et al. (2015)</td></tr><tr><td>EU-AL-001</td><td>Vegetation Database of Albania</td><td>Michele De Sanctis</td><td>Giuliano Fanelli</td><td>31</td><td>De Sanctis et al. (2017)</td></tr><tr><td>EU-AT-001</td><td>Austrian Vegetation Database</td><td>Wolfgang Willner</td><td>Christian Berg</td><td>2,310</td><td>Willner et al. (2012)</td></tr><tr><td>EU-BE-002</td><td>INBOVEG</td><td>Els De Bie</td><td></td><td>119</td><td></td></tr><tr><td>EU-BG-001</td><td>Bulgarian Vegetation Database</td><td>Iva Apostolova</td><td>Desislava Sopotlieva</td><td>160</td><td>Apostolova et al. (2012)</td></tr><tr><td>EU-CH-005</td><td>Swiss Forest Vegetation Database</td><td>Thomas Wohlgemuth</td><td></td><td>2,134</td><td>Wohlgemuth (2012)</td></tr><tr><td>EU-CZ-001</td><td>Czech National Phytosociological Database</td><td>Milan Chytrý</td><td>Ilona Knollová</td><td>1,287</td><td>Chytrý & Rafajová (2003)</td></tr><tr><td>EU-DE-001</td><td>VegMV</td><td>Florian Jansen</td><td>Christian Berg</td><td>15</td><td>Jansen et al. (2012)</td></tr><tr><td>EU-DE-013</td><td>VegetWeb Germany</td><td>Florian Jansen</td><td>Jörg Ewald</td><td>587</td><td>Ewald et al. (2012)</td></tr><tr><td>EU-DE-014</td><td>German Vegetation Reference Database (GVRD)</td><td>Ute Jandt</td><td>Helge Bruelheide</td><td>762</td><td>Jandt & Bruelheide (2012)</td></tr></tbody></table>

(Continues)
<table><caption>TABLE 1 (Continued)</caption><thead><tr><th>GIVD ID</th><th>Dataset name</th><th>Custodian</th><th>Deputy custodian</th><th>No. open-access plots</th><th>Reference</th></tr></thead><tbody><tr><td>EU-DK-002</td><td>National Vegetation Database of Denmark</td><td>Jesper Erenskjold Moeslund</td><td>Rasmus Ejrnæs</td><td>332</td><td></td></tr><tr><td>EU-ES-001</td><td>Iberian and Macaronesian Vegetation Information System (SIVIM) - Wetlands</td><td>Aaron Pérez-Haase</td><td>Xavier Font</td><td>580</td><td></td></tr><tr><td>EU-FR-003</td><td>SOPHY</td><td>Emmanuel Garbolino</td><td>Patrice De Ruffray</td><td>7,986</td><td>Garbolino et al. (2012)</td></tr><tr><td>EU-GB-001</td><td>UK National Vegetation Classification Database</td><td>John S. Rodwell</td><td></td><td>3,182</td><td></td></tr><tr><td>EU-GR-001</td><td>KRITI</td><td>Erwin Bergmeier</td><td></td><td>22</td><td></td></tr><tr><td>EU-GR-005</td><td>Hellenic Natura 2000 Vegetation Database (HelNatVeg)</td><td>Panayotis Dimopoulos</td><td>Ioannis Tsiripidis</td><td>620</td><td>Dimopoulos &amp; Tsiripidis (2012)</td></tr><tr><td>EU-GR-006</td><td>Hellenic Woodland Database</td><td>Ioannis Tsiripidis</td><td>Georgios Fotiadis</td><td>17</td><td>Fotiadis et al. (2012)</td></tr><tr><td>EU-HR-001</td><td>Phytosociological Database of Non-Forest Vegetation in Croatia</td><td>Zvjezdana Stančić</td><td></td><td>193</td><td>Stančić (2012)</td></tr><tr><td>EU-HR-002</td><td>Croatian Vegetation Database</td><td>Željko Škvorc</td><td>Daniel Krstonošić</td><td>585</td><td></td></tr><tr><td>EU-HU-003</td><td>CoenoDat Hungarian Phytosociological Database</td><td>János Csiky</td><td>Zoltán Botta-Dukát</td><td>46</td><td>Lájer et al. (2008)</td></tr><tr><td>EU-IT-001</td><td>VegItaly</td><td>Roberto Venanzoni</td><td>Flavia Landucci</td><td>754</td><td>Landucci et al. (2012)</td></tr><tr><td>EU-IT-010</td><td>Vegetation database of Habitats in the Italian Alps - HabItAlp</td><td>Laura Casella</td><td>Pierangela Angelini</td><td>247</td><td>Casella et al. (2012)</td></tr><tr><td>EU-IT-011</td><td>Vegetation-Plot Database Sapienza University of Rome (VPD-Sapienza)</td><td>Emiliano Agrillo</td><td>Fabio Attorre</td><td>967</td><td>Agrillo et al. (2017)</td></tr><tr><td>EU-LT-001</td><td>Lithuanian Vegetation Database</td><td>Valerijus Rašomavičius</td><td>Domas Uogintas</td><td>81</td><td></td></tr><tr><td>EU-LV-001</td><td>Semi-natural Grassland Vegetation Database of Latvia</td><td>Solvita Rūsiņa</td><td></td><td>369</td><td>Rūsiņa (2012)</td></tr><tr><td>EU-MK-001</td><td>Vegetation Database of the Republic of Macedonia</td><td>Renata Čušterevska</td><td></td><td>28</td><td></td></tr><tr><td>EU-NL-001</td><td>Dutch National Vegetation Database</td><td>Stephan M. Hennekens</td><td>Joop H. J. Schaminée</td><td>1,098</td><td>Schaminée et al. (2006)</td></tr><tr><td>EU-PL-001</td><td>Polish Vegetation Database</td><td>Zygmunt Kącki</td><td>Grzegorz Swacha</td><td>692</td><td>Kącki &amp; Śliwiński (2012)</td></tr><tr><td>EU-RO-007</td><td>Romanian Forest Database</td><td>Adrian Indreica</td><td>Pavel Dan Turtureanu</td><td>166</td><td>Indreica et al. (2017)</td></tr><tr><td>EU-RO-008</td><td>Romanian Grassland Database</td><td>Eszter Ruprecht</td><td>Kiril Vassilev</td><td>82</td><td>Vassilev et al. (2018)</td></tr><tr><td>EU-RS-002</td><td>Vegetation Database Grassland Vegetation of Serbia</td><td>Svetlana Aćić</td><td>Zora Dajić Stevanović</td><td>217</td><td>Aćić et al. (2012)</td></tr><tr><td>EU-RU-002</td><td>Lower Volga Valley Phytosociological Database</td><td>Valentin Golub</td><td>Andrey Chuvashov</td><td>383</td><td>Golub et al. (2012)</td></tr><tr><td>EU-RU-003</td><td>Vegetation Database of the Volga and the Ural Rivers Basins</td><td>Tatiana Lysenko</td><td></td><td>174</td><td>Lysenko et al. (2012)</td></tr><tr><td>EU-RU-011</td><td>Vegetation Database of Tatarstan</td><td>Vadim Prokhorov</td><td>Maria Kozhevnikova</td><td>206</td><td>Prokhorov et al. (2017)</td></tr><tr><td>EU-SI-001</td><td>Vegetation Database of Slovenia</td><td>Urban Šilc</td><td>Filip Kůzmič</td><td>1,029</td><td>Šilc (2012)</td></tr><tr><td>EU-SK-001</td><td>Slovak Vegetation Database</td><td>Milan Valachovič</td><td>Jozef Šibík</td><td>2,394</td><td>Šibík (2012)</td></tr><tr><td>EU-UA-001</td><td>Ukrainian Grasslands Database</td><td>Anna Kuzemko</td><td>Yulia Vashenyak</td><td>301</td><td>Kuzemko (2012)</td></tr><tr><td>EU-UA-006</td><td>Vegetation Database of Ukraine and Adjacent Parts of Russia</td><td>Viktor Onyshchenko</td><td>Vitaliy Kolomiychuk</td><td>96</td><td></td></tr><tr><td>NA-00-002</td><td>Tree Biodiversity Network (BIOTREE-NET)</td><td>Luis Cayuela</td><td></td><td>241</td><td>Cayuela et al. (2012)</td></tr></tbody></table>

(Continues)
<table><caption>TABLE 1 (Continued)</caption><thead><tr><td>GIVD ID</td><td>Dataset name</td><td>Custodian</td><td>Deputy custodian</td><td>No. open-access plots</td><td>Reference</td></tr></thead><tbody><tr><td>NA-CA-003</td><td>Database of Timberline Vegetation in NW North America</td><td>Viktoria Wagner</td><td>Toby Spribille</td><td>63</td><td>Wagner et al. (2014)</td></tr><tr><td>NA-CA-004</td><td>Understory of Sugar Maple Dominated Stands in Quebec and Ontario (Canada)</td><td>Isabelle Aubin</td><td></td><td>13</td><td>Aubin et al. (2007)</td></tr><tr><td>NA-CA-005</td><td>Boreal Forest of Canada</td><td>Philippe Marchand</td><td>Yves Bergeron</td><td>57</td><td>Harper et al. (2003)</td></tr><tr><td>NA-GL-001</td><td>Vegetation Database of Greenland</td><td>Birgit Jedrzejek</td><td>Fred J. A. Daniëls</td><td>441</td><td>Sieg et al. (2006)</td></tr><tr><td>NA-US-002</td><td>VegBank</td><td>Robert K. Peet</td><td>Michael T. Lee</td><td>14,965</td><td>Peet, Lee, Jennings, et al. (2012)</td></tr><tr><td>NA-US-006</td><td>Carolina Vegetation Survey Database</td><td>Robert K. Peet</td><td>Michael T. Lee</td><td>3,263</td><td>Peet, Lee, Boyle, et al. (2012)</td></tr><tr><td>NA-US-014</td><td>Alaska-Arctic Vegetation Archive</td><td>Donald A. Walker</td><td>Amy Breen</td><td>771</td><td>Walker et al. (2016)</td></tr><tr><td>SA-00-002</td><td>VegPáramo</td><td>Gwendolyn Peyre</td><td>Xavier Font</td><td>2,010</td><td>Peyre et al. (2015)</td></tr><tr><td>SA-AR-002</td><td>Vegetation Database of Central Argentina</td><td>Melisa Giorgis</td><td>Alicia T. R. Acosta</td><td>86</td><td></td></tr><tr><td>SA-BO-003</td><td>Bolivia Forest Plots</td><td>Michael Kessler</td><td>Sebastian Herzog</td><td>44</td><td></td></tr><tr><td>SA-BR-002</td><td>Forest Inventory, State of Santa Catarina, Brazil (IFFSC Project)</td><td>Alexander Christian Vibrans</td><td>André Luís de Gasper</td><td>1,561</td><td>Vibrans et al. (2020)</td></tr><tr><td>SA-BR-003</td><td>Grasslands of Rio Grande do Sul, Brazil</td><td>Eduardo Vélez-Martin</td><td>Valério D. Pillar</td><td>306</td><td></td></tr><tr><td>SA-BR-004</td><td>Grassland Database of Campos Sulinos</td><td>Gerhard E. Overbeck</td><td>Valério D. Pillar</td><td>147</td><td></td></tr><tr><td>SA-CL-002</td><td>SSAForests_Plots_db</td><td>Alvaro G. Gutiérrez</td><td></td><td>155</td><td></td></tr><tr><td>SA-CL-003</td><td>Chilean Park Transects - Fondecyt 1040528</td><td>Aníbal Pauchard</td><td>Alicia Marticorena</td><td>44</td><td>Pauchard et al. (2013)</td></tr><tr><td>SA-EC-001</td><td>Ecuador Forest Plot Database</td><td>Jürgen Homeier</td><td></td><td>166</td><td></td></tr></tbody></table>

Note: Datasets are ordered based on their ID in the Global Index of Vegetation Databases (GVID ID).

structure (e.g., vegetation type, percentage cover of each vegetation layer), vegetation type, and naturalness level (i.e., whether a plot belongs to the same formation that would occupy the site without human interference). Plots in Europe are also classified according to the European Nature Information System (EUNIS) habitat classification (column 'ESY'), based on the habitat classification expert system (ESY, Chytrý et al., 2020). For each vegetation plot, we further provide information on the dataset it originates from, based on the IDs used in GIVD (Dengler et al., 2011). We also report four binary fields describing whether a plot belongs to the three resampling iterations (columns 'Resample_1', 'Resample_2', 'Resample_3'), or to the first resampling iteration after the inclusion of replacement plots (column 'Resample_1_consensus'). A brief summary of all the 47 variables in the header matrix is provided in Table 2.

The 'DT' matrix contains data on the species composition of each plot. It is structured in a long format and contains 1,945,384 records from 42,680 vascular plant taxa, mostly resolved at the species level. For each record, we report both the taxon name as originally contributed by the data custodian (column 'Original物种'), and the taxon

name after taxonomic standardization (column 'Species'). For details on the taxonomic standardization, please see section 'Technical validation' below. For each species we also provided cover/abundance values. These follow different standards across the datasets constituting the sPlot database. We, therefore, provide both the cover/abundance value as reported in the original data (column 'Original_abundance'), together with the abundance scale that was originally used (column 'Abundance_scale'). This can take seven values: 'CoverPerc' = percentage cover; 'pa' = presence-absence; 'x_BA' = basal area (m²/ha, only for woody species); 'x_IC' = individual count, that is, number of individuals in plot; 'x_SC' = stem count, that is, number of stems in plot; 'x_IV' = importance value index; and 'x' = presence frequency. The great majority of entries, however, use the percentage cover scale (n = 1,709,000). Finally, for each entry, we calculated a 'Relative_cover', that is, the cover/abundance of a given taxon divided by the total cover/abundance of all taxa in that vegetation plot.

The 'CWM_CWV' matrix contains the community-weighted means and variances calculated for each of the 18 functional traits mentioned above. It also contains three additional columns. The
<figure><img src="image_6.png" /><figcaption>FIGURE 3 Distribution of vegetation plots in the first resampling iteration of sPlotOpen (n = 49,787) in the two-dimensional climatic space represented by mean annual temperature and mean annual precipitation. Left: plots are colour coded based on sBiomes, that is, sPlot's definition of biomes (Bruelheide et al., 2019), which derives from Schultz's (2005) ecozones, modified to include also the alpine biome from Körner et al. (2017). Right: the same plots superimposed onto Whittaker's biomes (Whittaker, 1975), as adapted by Ricklefs (2008) and plotted using the R package 'plotbiomes' [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

column 'Species_richness' shows the number of species recorded in each plot. The columns 'Trait New coverage New cover' and 'Trait New coverage Old cover' provide, respectively, the proportion of total cover and the proportion of species in a plot for which functional trait information was available. In total, functional trait information was available for 21,854 species. As functional trait information was based on gap-filled data (see above), each of these 21,854 species had information for all the 18 functional traits. The average proportion of species in each plot for which functional trait information was available is .85 (median = .95). For 42,012 plots, the coverage was complete, while we do not have functional trait information for any of the species occurring in 482 plots. When considering relative cover, the average trait coverage is .87, with 74,151 plots having functional trait information for species cumulatively accounting for more than 80% of relative cover. When considering the number of species, 68,041 plots have functional trait information for 80% or more of the species occurring in that plot.

sPlotOpen contains two additional objects. The 'metadata' matrix contains plot-level metadata, which provide information on the origin of each individual vegetation plot. This object contains 15 columns, with information on the dataset of origin (column 'GIVD_ID' - Dengler et al., 2011), author or surveyor names (columns 'Releve_author' and 'Releve_coauthor'), bibliographic references both at the dataset (column 'DB_BIBTEXKEY') and plot level ('Plot_Biblioreference' and 'BIBTEXKEY'), when available. Similarly, the column 'Project_name' provides information on the project in which a vegetation plot was originally recorded. When available, we also provide information on the numbering of the plots in the publication where they originally appeared (columns 'Nr_table_in_publ', 'Nr_releve_in_table'), or in the dataset where they were initially stored ('Original_nr_in_database'). In the case of nested plots (n = 1,851), we also provide the original plot and subplot IDs (columns: 'Original plot ID', 'Original subplot ID'). The last two columns

report plot-level 'Remarks', and the unique identifier produced by TURBOVEG when the vegetation plot was first stored ('GUID'). TURBOVEG is a program specifically designed to store, maintain and export vegetation plot data (https://www.synbiosys.alterra.nl/turboveg; Hennekens & Schaminée, 2001).

Finally, the object 'references' contains all the bibliographic references formatted according to a BibTex standard. Each reference is tagged with a key corresponding to the fields 'DB_BIBTEXKEY' and 'BIBTEXKEY' in the metadata. We further provide an R function ('sPlotOpen_citation') to create reference lists, based on a selection of plots and/or datasets.

Except for the 'reference' file (format.bib), all objects/matrices are provided in tab-delimited.txt files. All objects, including the 'sPlotOpen_citation' function, are also compiled inside a .RData object.

## 5 | TECHNICAL VALIDATION

The original sPlot database has a nested structure and consists of several individual datasets, each validated and maintained by its respective dataset custodian. In many cases, individual datasets are also collections whose vegetation plots were provided by their respective owners (the person who performed the actual vegetation survey) or by someone who digitized the original data from the scientific published or grey literature. We obviously have no direct control over the individual vegetation plots that we provide here in sPlotOpen. Yet, all these vegetation plots stem from trained professional botanists, or published scientific work, and are accompanied by detailed information on the sampling protocols used, thus ensuring data quality and reliability.

Before integration into the sPlot database, each dataset was further checked for consistency. If the dataset was in a different format, we converted it to a TURBOVEG 2 dataset (Hennekens & Schaminée,
<table><caption>TABLE 2 Description of the variables contained in the 'header' matrix, together with their range (if numeric) or possible levels (if nominal or binary) and the number of non-empty (i.e., non NA) records</caption><thead><tr><th>Variable</th><th>Range/levels</th><th>Unit of measurement</th><th>No. of plots with information</th><th>Type</th></tr></thead><tbody><tr><td>GIVD_ID</td><td>see Table 1</td><td></td><td>95,104</td><td>n</td></tr><tr><td>Dataset</td><td>see Table 1</td><td></td><td>95,104</td><td>n</td></tr><tr><td>Continent</td><td>Africa, Asia, Europe, North America, Oceania, South America</td><td></td><td>95,104</td><td>n</td></tr><tr><td>Country</td><td></td><td></td><td>95,104</td><td>n</td></tr><tr><td>Biome</td><td>Alpine, Boreal zone, Dry midlatitudes, Dry tropics and subtropics, Polar and subpolar zone, Subtropics with year-round rain, Subtropics with winter rain, Temperate midlatitudes, Tropics with summer rain, Tropics with year-round rain</td><td></td><td>95,104</td><td>n</td></tr><tr><td>Date_of_recording</td><td>05-07-1888 - 03-02-2015</td><td>dd-mm-yyyy</td><td>80,085</td><td>d</td></tr><tr><td>Latitude</td><td>-54.82303 - 80.149116</td><td>° (WGS84)</td><td>95,104</td><td>q</td></tr><tr><td>Longitude</td><td>-162.741433 - 176.4221</td><td>° (WGS84)</td><td>95,104</td><td>q</td></tr><tr><td>Location_uncertainty</td><td>1-2,750</td><td>m</td><td>95,075</td><td>q</td></tr><tr><td>Releve_area</td><td>0.03-40,000</td><td>m²</td><td>67,022</td><td>q</td></tr><tr><td>Plant_recarded</td><td>All vascular plants, All trees & dominant understory, Dominant trees, Only dominant species, Dominant woody plants &gt;= 2.5 cm dbh, All woody plants, Woody plants &gt;= 1 cm dbh, Woody plants &gt;= 2.5 cm dbh, Woody plants &gt;= 5 cm dbh, Woody plants &gt;= 10 cm dbh, Woody plants &gt;= 20 cm dbh, Woody plants &gt;= 1 m height, Not specified</td><td></td><td>95,104</td><td>n</td></tr><tr><td>Elevation</td><td>-30 - 5,960</td><td>m a.s.l.</td><td>62,968</td><td>q</td></tr><tr><td>Aspect</td><td>1-360</td><td>°</td><td>42,178</td><td>q</td></tr><tr><td>Slope</td><td>0-90</td><td>°</td><td>51,246</td><td>q</td></tr><tr><td>is_forest</td><td>FALSE = 45,735; TRUE = 38,282</td><td></td><td>84,017</td><td>b</td></tr><tr><td>ESY</td><td></td><td></td><td>39,632</td><td>n</td></tr><tr><td>Naturalness</td><td>1 = Natural, 2 = Semi-natural</td><td></td><td>60,192</td><td>o</td></tr><tr><td>Forest</td><td>FALSE = 36,282; TRUE = 33,170</td><td></td><td>69,452</td><td>b</td></tr><tr><td>Shrubland</td><td>FALSE = 58,245; TRUE = 11,207</td><td></td><td>69,452</td><td>b</td></tr><tr><td>Grassland</td><td>FALSE = 33,800; TRUE = 35,652</td><td></td><td>69,452</td><td>b</td></tr><tr><td>Wetland</td><td>FALSE = 59,196; TRUE = 10,256</td><td></td><td>69,452</td><td>b</td></tr><tr><td>Sparse_vegetation</td><td>FALSE = 66,177; TRUE = 3,275</td><td></td><td>69,452</td><td>b</td></tr><tr><td>Cover_total</td><td>1-990</td><td>%</td><td>19,407</td><td>q</td></tr><tr><td>Cover_treeLayer</td><td>0.5-150</td><td>%</td><td>12,094</td><td>q</td></tr><tr><td>Cover_shrubLayer</td><td>0.5-170</td><td>%</td><td>16,804</td><td>q</td></tr><tr><td>Cover_herbLayer</td><td>0.2-199</td><td>%</td><td>29,668</td><td>q</td></tr><tr><td>Cover_mossLayer</td><td>1-100</td><td>%</td><td>9,681</td><td>q</td></tr><tr><td>Cover_lichenLayer</td><td>1-90</td><td>%</td><td>708</td><td>q</td></tr><tr><td>Cover_algaeLayer</td><td>1-100</td><td>%</td><td>41</td><td>q</td></tr><tr><td>Cover_litterLayer</td><td>1-107</td><td>%</td><td>3,161</td><td>q</td></tr><tr><td>Cover_bare_rocks</td><td>1-100</td><td>%</td><td>2,747</td><td>q</td></tr><tr><td>Cover_cryptogams</td><td>1-90</td><td>%</td><td>772</td><td>q</td></tr><tr><td>Cover_bare_soil</td><td>0-99</td><td>%</td><td>2,746</td><td>q</td></tr><tr><td>HeightTrees_highest</td><td>1-99</td><td>m</td><td>8,220</td><td>q</td></tr><tr><td>HeightTrees_lowest</td><td>1-90</td><td>m</td><td>447</td><td>q</td></tr><tr><td>Height_shrubs_highest</td><td>0.1-9.9</td><td>m</td><td>3,389</td><td>q</td></tr></tbody></table>

(Continues)
<table><caption>TABLE 2 (Continued)</caption><thead><tr><th>Variable</th><th>Range/levels</th><th>Unit of measurement</th><th>No. of plots with information</th><th>Type</th></tr></thead><tbody><tr><td>Height_shrubs_lowest</td><td>0.1-9</td><td>m</td><td>263</td><td>q</td></tr><tr><td>Height_herbs_average</td><td>0.1-600</td><td>cm</td><td>5,901</td><td>q</td></tr><tr><td>Height_herbs_lowest</td><td>1-150</td><td>cm</td><td>490</td><td>q</td></tr><tr><td>Height_herbs_highest</td><td>1-600</td><td>cm</td><td>1,083</td><td>q</td></tr><tr><td>SoilClim_PC1</td><td>-6.233 - 8.172</td><td></td><td>95,104</td><td>q</td></tr><tr><td>SoilClim_PC2</td><td>-4.824 - 15.466</td><td></td><td>95,104</td><td>q</td></tr><tr><td>Resample_1</td><td>FALSE = 45,317; TRUE = 49,787</td><td></td><td>95,104</td><td>b</td></tr><tr><td>Resample_2</td><td>FALSE = 45,293; TRUE = 49,811</td><td></td><td>95,104</td><td>b</td></tr><tr><td>Resample_3</td><td>FALSE = 45,315; TRUE = 49,789</td><td></td><td>95,104</td><td>b</td></tr><tr><td>Resample_1_consensus</td><td>FALSE = 41,842; TRUE = 53,262</td><td></td><td>95,104</td><td>b</td></tr></tbody></table>

Note: dbh = diameter at breast height. Variable types can be n = nominal (i.e., qualitative variable); o = ordinal; q = quantitative; b = binary (i.e., Boolean); or d = date. Additional details on the variables are in Bruelheide et al. (2019). Global Index of Vegetation Databases (GIVD) codes derive from Dengler et al. (2011). Biomes refer to Schultz (2005), modified to include also the world mountain regions (Körner et al., 2017). The column ESY refers to the European Nature Information System (EUNIS) Habitat Classification expert system (ESY, Chytrý et al., 2020).

2001). During this conversion, we checked that all datasets contained the required metadata information, and cross-checked that each plot was located within the geographical scope of its respective dataset. All individual TURBOVEG 2 datasets were then integrated into a TURBOVEG 3 database, and exported to comma-separated files. Finally, we harmonized all the taxonomic names from all datasets, based on sPlot's taxonomic backbone (Purschke, 2017). This backbone matched all the taxonomic names (without nomenclatural authors) from all datasets in sPlot v2.1 and TRY v3.0 (Kattge et al., 2020) to their resolved version based on the Taxonomic Name Resolution Service web application (TNRS version 4.0; Boyle et al., 2013). This allowed us to (a) harmonize all datasets to a common nomenclature and (b) link the sPlot database to the TRY database (Kattge et al., 2020). The final backbone only retained matched taxonomic names at the rank of species or higher. Additional detail on the taxonomic resolution is reported in Bruelheide et al. (2019), while a description of the workflow, including R-code, is available in Purschke (2017).

## 6 | USAGE NOTES

The sPlotOpen database can be downloaded from https://doi.org/10.25829/idiv.3474-40-3292. A short vignette introducing the use of sPlotOpen in R can be found in Supporting Information Appendix S1. Users are urged to cite the original sources when using sPlotOpen in addition to the present paper (see Table 1). For two datasets (AF-00-009, AF-CD-001), the identification of taxa at species level is still in progress. Data on lichens and mosses, where available (e.g., dataset NA-GL-001), can be obtained on request from the respective dataset custodian or sPlot coordinator. As most of the constitutive datasets remain under continuous development, sPlotOpen users are encouraged to get in touch with the custodian(s) of the data they are planning to use (the updated list of custodian names is maintained on the sPlot website).

The use of sPlotOpen comes with a number of warnings. First, sPlotOpen was resampled in a way that maximizes the compositional variability of vegetation in different environmental conditions. As such, sPlotOpen should not be considered as representative of the spatial distribution of plant communities, especially when the focus has a local or regional spatial extent. Second, for most regions data were collected opportunistically, and without a randomized sampling design. This might lead to some vegetation types being oversampled in some regions, but undersampled in other regions, which might affect the output of species distribution models, especially at local or regional spatial extents. Third, not all plots were sampled using the same plot size, and some plots, mostly located in tropical regions, only contain data on woody species. This should be accounted for when exploring biodiversity patterns or comparing biodiversity indices (e.g., species richness, beta diversity) across plots or regions. Finally, a small fraction of plots are nested subsets of larger plots. Depending on the application, this might or might not represent a problem. Nested plots can be identified using the information in the 'metadata' matrix. The most appropriate way to deal with these issues depends on the problem being analysed. Users are, therefore, invited to carefully consider the limitations above when designing applications relying on sPlotOpen.

The data described here represent the subset of sPlot for which we were able to secure permission for making these data open. Additional data from sPlot are available under sPlot's Governance and Data Property Rules (https://www.idiv.de/en/splot). Using the full sPlot dataset is also recommended if a stratification is desired that is different from the environmental factors used here, for example by geographical region or plot size.

## ACKNOWLEDGMENTS

The authors are grateful to the thousands of vegetation scientists who sampled vegetation plots in the field or digitized them into regional, national or international databases. The authors also
appreciate the support of the German Research Foundation for funding sPlot as one of the iDiv (DFG FZT 118, 202548816) research platforms, as well as for funding the position of Francesco Maria Sabatini and the organization of three workshops through the sDiv calls. The authors acknowledge this support with naming the database 'sPlot', where the 's' refers to the sDiv synthesis workshops. The authors are also grateful to Anahita Kazem and iDiv's Data & Code Unit for assistance with curation and archiving of the dataset.

The study has been supported by the TRY initiative on plant traits (http://www.try-db.org). The TRY initiative and database is hosted, developed and maintained by J. Kattge and G. Bönisch (Max Planck Institute for Biogeochemistry, Jena, Germany). TRY is currently supported by DIVERSITAS/Future Earth and iDiv Halle-Jena-Leipzig. Jens Kattge acknowledges support by the Max Planck Institute for Biogeochemistry (Jena, Germany), Future Earth, iDiv Halle-Jena-Leipzig and the EU H2020 project BACI, Grant No. 640176.

Isabelle Aubin was funded through the Natural Sciences and Engineering Research Council of Canada and Ontario Ministry of Natural Resources and Forestry. Yves Bergeron was funded through the Natural Sciences and Engineering Research Council of Canada. Idoia Biurrun was funded by the Basque Government (IT936-16). Anne Bjorkman thanks the Herschel Island-Qikiqtaruk Territorial Park management, Catherine Kennedy, Dorothy Cooley, Jill F. Johnstone, Cameron Eckert and Richard Gordon for establishing the ecological monitoring programme. Funding was provided by Herschel Island-Qikiqtaruk Territorial Park. Luis Cayuela was supported by project BIOCON08_044 funded by Fundación BBVA (Banco Bilbao Vizcaya Argantiera). Milan Chytrý, Flavia Landucci, Corrado Marcenò and Tomáš Peterka were supported by the Czech Science Foundation (project no. 19-28491X). Brian Enquist thanks the following individuals and institutions for contributing data to sPlot via the SALVIAS database: Mauricio Bonifacino, Saara DeWalt, Timothy Killeen, Susan Letcher, Nigel Pitman, Cam Webb, The Missouri Botanical Garden, RAINFOR and the Amazon Forest Inventory Network. Alvaro G. Gutiérrez was funded by Project FORECOFUN-SSA PIEF-GA-2010-274798 and FONDECYT 1200468. Mohamed Z. Hatim thanks Kamal Shaltout and Joop Schaminée for MSc thesis supervision, and Joop Schaminée for support and funding from the Prince Bernard Culture Fund Prize for Nature Conservation. Jürgen Homeier received funding from BMBF (Federal Ministry of Education and Science of Germany) and the German Research Foundation (DFG Ho3296-2, DFG Ho3296-4). Borja Jiménez-Alfaro was funded by the Spanish Research Agency through grant AEI/10.13039/501100011033. Dirk N. Karger received funding from: the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) internal grant exCHELSA and ClimEx, the Joint Biodiversa COFUND project 'FeedBaCks' and 'Futureweb', the Swiss Data Science Projects: SPEEDMIND, and COMECO, and the Swiss National Science Foundation (20BD21_184131). Hjalmar Kühl gratefully acknowledges the Pan African team and funding by the Max Planck Society and Krekeler Foundation. Attila

Lengyel was supported by the National Research, Development and Innovation Office, Hungary (PD-123997). Tatiana Lysenko was funded by the Russian Foundation for Basic Research (Grant No. 16-04-00747a). Alireza Naqinezhad is supported by a master grant from the University of Mazandaran. Jérôme Munzinger was supported by the French National Research Agency (ANR) with grants INC (ANR-07-BDIV-0008), BIONECAL (ANR-07-BDIV-0006) & ULTRABIO (ANR-07-BDIV-0010), by the National Geographic Society (Grant 7579-04), and with funding and authorizations of North and South Provinces of New Caledonia. Arkadiusz Nowak received support from the National Science Centre, Poland, grant no. 2017/25/B/NZ8/00572. Gerhard E. Overbeck acknowledges support from Brazil's National Council of Scientific and Technological Development (CNPq, grant 310022/2015-0). Meelis Pärtel was supported by the Estonian Research Council (PRG609) and European Regional Development Fund (Centre of Excellence EcolChange). Robert Peet acknowledges the support from the National Center for Ecological Analysis and Synthesis, the North Carolina Ecosystem Enhancement Program, the U.S. Forest Service, and the U.S. National Science Foundation (DBI-9905838, DBI-0213794). Josep Peñuelas acknowledges the financial support from the European Research Council Synergy grant ERC-SyG-2013-610028 IMBALANCE-P. Petr Petřík and Jiri Dolezal acknowledge the support of the long-term research development project No. RVO 67985939 of the Czech Academy of Sciences. Oliver Phillips was funded by an ERC Advanced Grant (291585, 'T-FORCES') and a Royal Society-Wolfson Research Merit Award. Valério D. Pillar was supported by the Brazil's National Council of Scientific and Technological Development (CNPq, grant 307689/2014-0). Solvita Rūsina was supported by the University of Latvia grant AAP2016/ B041//Zd2016/AZ03 within the 'Climate change and sustainable use of natural resources' framework. Franziska Schrodt was supported by the University of Minnesota Institute on the Environment Discovery Grant, the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig grant (50170649_#7) and the University of Nottingham Anne McLaren Fellowship. Jozef Šibík was funded by The Slovak Research and Development Agency grant no. APVV16-0431. Jens-Christian Svenning considers this work a contribution to his VILLUM Investigator project 'Biodiversity Dynamics in a Changing World' funded by VILLUM FONDEN (grant 16549). Kim André Vanselow would like to thank W. Bernhard Dickoré for the help in the identification of plant species and acknowledges the financial support from the Volkswagen Foundation (AZ I/81 976) and the German Research Foundation (DFG VA 749/1-1, DFG VA 749/4-1). Evan Weiher was funded by NSF DEB-0415383, UWEC-ORSP, and UWEC-BCDT. Work by Karsten Wesche was supported by the German Research Foundation (DFG WE 2601/3-1,3-2, 4-1,4-2) and by the German Ministry for Science and Education (BMBF, CAME 03G0808A). Susan Wiser was funded by the New Zealand (NZ) Ministry for Business, Innovation and Employment's Strategic Science Investment Fund.

This paper is dedicated to the memory of Dr. Ching-Feng (Woody) Li.
## CONFLICT OF INTEREST

The authors declare no competing interests.

## AUTHOR CONTRIBUTIONS

FMS wrote the first draft of the manuscript, with considerable input from JL and HB. JL and TH wrote the resampling algorithm. FMS set up the GitHub projects, curated the database, and produced the graphs. He also coordinated the sPlot consortium. SMH wrote the TURBOVEG software, which holds the sPlot database. JKa provided the trait data from TRY and FSc performed the trait data gap filling. HB secured the funding for sPlot as a strategic project of iDiv. All other authors contributed data and/or helped set up the database and/or helped develop the resampling algorithm. All authors contributed to revising and approved the manuscript.

## DATA AVAILABILITY STATEMENT

The R code used to produce sPlotOpen from the sPlot v2.1 database is contained in the sPlotOpen_code GitHub repository: https://github.com/fmsabatini/sPlotOpen Code. This manuscript was produced using the Manubot workflow (Himmelstein et al., 2019). The code for reproducing this manuscript is stored in the sPlotOpen-manuscript GitHub repository: https://github.com/fmsabatini/sPlotOpen_Manuscript.

## ORCID

Francesco Maria Sabatini ID https://orcid.org/0000-0002-7202-7697
Jonathan Lenoir ID https://orcid.org/0000-0003-0638-9582
Tarek Hattab ID https://orcid.org/0000-0002-1420-5758
Elise Aimee Arnst ID https://orcid.org/0000-0003-2388-7428
Milan Chytry ID https://orcid.org/0000-0002-8122-3075
Jürgen Dengler ID https://orcid.org/0000-0003-3221-660X
Stephan M. Hennekens ID https://orcid.org/0000-0003-1221-0323
Ute Jandt ID https://orcid.org/0000-0002-3177-3669
Florian Jansen ID https://orcid.org/0000-0002-0331-5185
Borja Jiménez-Alfaro ID https://orcid.org/0000-0001-6601-9597
Jens Kattge ID https://orcid.org/0000-0002-1022-8469
Valério D. Pillar ID https://orcid.org/0000-0001-6408-2891
Oliver Purschke ID https://orcid.org/0000-0003-0444-0882
Brody Sandel ID https://orcid.org/0000-0003-2162-6902
Tsipe Aavik ID https://orcid.org/0000-0001-5232-3950
Svetlana Aćić ID https://orcid.org/0000-0001-6553-3797
Alicia T. R. Acosta ID https://orcid.org/0000-0001-6572-3187
Emiliano Agrillo ID https://orcid.org/0000-0003-2346-8346
Miguel Alvarez ID https://orcid.org/0000-0003-1500-1834
Mohammed A. S. Arfin Khan ID https://orcid.org/0000-0001-6275-7023
Fabio Attorre ID https://orcid.org/0000-0002-7744-2195
Isabelle Aubin ID https://orcid.org/0000-0002-5953-1012
Marijn Bauters ID https://orcid.org/0000-0003-0978-6639
Yves Bergeron ID https://orcid.org/0000-0003-3707-3687
Erwin Bergmeier ID https://orcid.org/0000-0002-6118-4611
Idoia Biurrun ID https://orcid.org/0000-0002-1454-0433
Anne D. Bjorkman ID https://orcid.org/0000-0003-2174-7800
Gianmaria Bonari ID https://orcid.org/0000-0002-5574-6067

Viktoria Bondareva ID https://orcid.org/0000-0002-6676-5722
Jörg Brunet ID https://orcid.org/0000-0003-2667-4575
Andraž Čarni ID https://orcid.org/0000-0002-8909-4298
Laura Casella ID https://orcid.org/0000-0003-2550-3010
Luis Cayuela ID https://orcid.org/0000-0003-3562-2662
Victor Chepinoga ID https://orcid.org/0000-0003-3809-7453
János Csiky ID https://orcid.org/0000-0002-7920-5070
Els De Bie ID https://orcid.org/0000-0001-7679-743X
André Luis de Gasper ID https://orcid.org/0000-0002-1940-9581
Michele De Sanctis ID https://orcid.org/0000-0002-7280-6199
Jiri Dolezal ID https://orcid.org/0000-0002-5829-4051
Mohamed Abd El-Rouf Mousa El-Sheikh ID https://orcid.org/0000-0002-0720-7448
Brian Enquist ID https://orcid.org/0000-0002-6124-7096
Jörg Ewald ID https://orcid.org/0000-0002-2758-9324
Richard Field ID https://orcid.org/0000-0003-2613-2688
Manfred Finckh ID https://orcid.org/0000-0003-2186-0854
Sophie Gachet ID https://orcid.org/0000-0002-3599-5189
Antonio Galán-de-Mera ID https://orcid.org/0000-0002-1652-5931
Hamid Gholizadeh ID https://orcid.org/0000-0002-3694-368X
Melisa Giorgis ID https://orcid.org/0000-0001-6126-6660
Valentin Golub ID https://orcid.org/0000-0003-3973-6608
Inger Greve Alsos ID https://orcid.org/0000-0002-8610-1085
Gregory Richard Guerin ID https://orcid.org/0000-0002-2104-6695
Alvaro G. Gutiérrez ID https://orcid.org/0000-0001-8928-3198
Sylvia Haider ID https://orcid.org/0000-0002-2966-0534
Mohamed Z. Hatim ID https://orcid.org/0000-0002-0872-5108
Bruno Hérault ID https://orcid.org/0000-0002-6950-7286
Norbert Hölzel ID https://orcid.org/0000-0002-6367-3400
Jürgen Homeier ID https://orcid.org/0000-0001-5676-3267
Anke Jentsch ID https://orcid.org/0000-0002-2345-8300
Norbert Jürgens ID https://orcid.org/0000-0003-3211-0549
Dirk Nikolaus Karger ID https://orcid.org/0000-0001-7770-6229
Ali Kavgaci ID https://orcid.org/0000-0002-4549-3668
Elizabeth Kearsley ID https://orcid.org/0000-0003-0046-3606
Michael Kessler ID https://orcid.org/0000-0003-4612-9937
Larisa Khanina ID https://orcid.org/0000-0002-8937-5938
Holger Kreft ID https://orcid.org/0000-0003-4471-8236
Hjalmar S. Kühl ID https://orcid.org/0000-0002-4440-9161
Anna Kuzemko ID https://orcid.org/0000-0002-9425-2756
Flavia Landucci ID https://orcid.org/0000-0002-6848-0384
Attila Lengyel ID https://orcid.org/0000-0002-1712-6748
Frederic Lens ID https://orcid.org/0000-0002-5001-0149
Débora Vanessa Lingner ID https://orcid.org/0000-0002-6391-9343
Hongyan Liu ID https://orcid.org/0000-0002-6721-4439
Tatiana Lysenko ID https://orcid.org/0000-0001-6688-1590
Miguel D. Mahecha ID https://orcid.org/0000-0003-3031-613X
Corrado Marcenò ID https://orcid.org/0000-0003-4361-5200
Vasiliy Martynenko ID https://orcid.org/0000-0002-9071-3789
Jesper Erenskjold Moeslund ID https://orcid.org/0000-0001-8591-7149
Abel Monteagudo Mendoza ID https://orcid.org/0000-0002-1047-845X
Ladislav Mucina ID https://orcid.org/0000-0003-0317-8886
Jonas V. Müller ID https://orcid.org/0000-0001-7049-3048
Jérôme Munzinger https://orcid.org/0000-0001-5300-2702

Jalil Noroozi https://orcid.org/0000-0003-4124-2359

Arkadiusz Nowak https://orcid.org/0000-0001-8638-0208

Gerhard E. Overbeck https://orcid.org/0000-0002-8716-5136

Meelis Pärtel https://orcid.org/0000-0002-5874-0138

Aníbal Pauchard https://orcid.org/0000-0003-1284-3163

Robert K. Peet https://orcid.org/0000-0003-2823-6587

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

Gwendolyn Peyre https://orcid.org/0000-0002-1977-7181

Oliver L. Phillips https://orcid.org/0000-0002-8993-6168

Valerijus Rašomavičius https://orcid.org/0000-0003-1314-4356

Rasmus Revermann https://orcid.org/0000-0002-7044-768X

Gonzalo Rivas-Torres https://orcid.org/0000-0002-2704-8288

Solvita Rūsina https://orcid.org/0000-0002-9580-4110

Marco Schmidt https://orcid.org/0000-0001-6087-6117

Franziska Schrodt https://orcid.org/0000-0001-9053-8872

Pavel Shirokikh https://orcid.org/0000-0003-1864-4878

Jozef Šibík https://orcid.org/0000-0002-5949-862X

Urban Šilc https://orcid.org/0000-0002-3052-699X

Željko Škvorc https://orcid.org/0000-0002-2848-1454

Marta Gaia Sperandii https://orcid.org/0000-0002-2507-5928

Jens-Christian Svenning https://orcid.org/0000-0002-3415-0862

Kim André Vanselow https://orcid.org/0000-0003-3299-6220

Eduardo Vélez-Martín https://orcid.org/0000-0001-8028-8953

Roberto Venanzoni https://orcid.org/0000-0002-7768-0468

Cyrille Violle https://orcid.org/0000-0002-2471-9226

Risto Virtanen https://orcid.org/0000-0002-8295-8217

Viktoria Wagner https://orcid.org/0000-0002-2665-9888

Donald A. Walker https://orcid.org/0000-0002-2665-9888

Donald M. Waller https://orcid.org/0000-0001-5377-3929

Timothy J. S. Whitfeld https://orcid.org/0000-0003-1850-6432

Wolfgang Willner https://orcid.org/0000-0003-1591-8386

Susan K. Wiser https://orcid.org/0000-0002-8938-8181

Thomas Wohlgemuth https://orcid.org/0000-0002-4623-0894

Martin Zobel https://orcid.org/0000-0001-7957-6704

Helge Bruelheide https://orcid.org/0000-0003-3135-0356

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## BIOSKETCH

sPlot is a collaborative initiative to integrate existing local and national vegetation-plot datasets into a global harmonized database. It was initiated in 2013, within the sDiv working group 'Plant trait-environment relationships across the world's biomes'. Since then, it has become established as the largest vegetation-plot database worldwide and coordinates a consortium of 251 individual active members, representing 167 local and national datasets. sPlot's overarching scientific goal is the exploration of all aspects of global plant community diversity, including taxonomic, functional and phylogenetic diversity, across biomes, vegetation types, taxonomic or functional guilds and scales. Central to sPlot's mission is the exploration of the relationships between environmental drivers, trait variation, and assembly processes in local plant communities worldwide.

## SUPPORTING INFORMATION

Additional Supporting Information may be found online in the Supporting Information section.

**How to cite this article:** Sabatini FM, Lenoir J, Hattab T, et al. sPlotOpen – An environmentally balanced, open-access, global dataset of vegetation plots. *Global Ecol Biogeogr.* 2021;30:1740–1764. https://doi.org/10.1111/geb.13346