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

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Authors

ORCID iconFrancesco Maria Sabatini1,2,†, ORCID iconJonathan Lenoir3,†, ORCID iconTarek Hattab4, ORCID iconElise Aimee Arnst5, ORCID iconMilan Chytrý6, ORCID iconJürgen Dengler1,7,8, Patrice De Ruffray9, ORCID iconStephan M. Hennekens10, ORCID iconUte Jandt1,2, Florian Jansen11, ORCID iconBorja Jiménez-Alfaro12, ORCID iconJens Kattge13, Aurora Levesley14, ORCID iconValério D. Pillar15, ORCID iconOliver Purschke16, Brody Sandel17, Fahmida Sultana18, ORCID iconTsipe Aavik19, ORCID iconSvetlana Aćić20, ORCID iconAlicia T.R. Acosta21, ORCID iconEmiliano Agrillo22, ORCID iconMiguel Alvarez23, Iva Apostolova24, ORCID iconMohammed A.S. Arfin Khan25, Luzmila Arroyo26, ORCID iconFabio Attorre27, ORCID iconIsabelle Aubin28, Arindam Banerjee29, Marijn Bauters30,31, ORCID iconYves Bergeron32, ORCID iconErwin Bergmeier33, ORCID iconIdoia Biurrun34, ORCID iconAnne D. Bjorkman35,36, ORCID iconGianmaria Bonari37, ORCID iconViktoria Bondareva38, ORCID iconJörg Brunet39, ORCID iconAndraž Čarni40,41, ORCID iconLaura Casella42, ORCID iconLuis Cayuela43, Tomáš Černý44, ORCID iconVictor Chepinoga45, ORCID iconJános Csiky46, Renata Ćušterevska47, ORCID iconEls De Bie48, André Luis de Gasper49, ORCID iconMichele De Sanctis27, Panayotis Dimopoulos50, ORCID iconJiri Dolezal51, Tetiana Dziuba52, ORCID iconMohamed Abd El-Rouf Mousa El-Sheikh53,54, Brian Enquist55, ORCID iconJörg Ewald56, Farideh Fazayeli57,58, ORCID iconRichard Field59, Manfred Finckh60, ORCID iconSophie Gachet61, ORCID iconAntonio Galán-de-Mera62,63,64, ORCID iconEmmanuel Garbolino65, ORCID iconHamid Gholizadeh66, ORCID iconMelisa Giorgis67, ORCID iconValentin Golub68, ORCID iconInger Greve Alsos69, John-Arvid Grytnes70, ORCID iconGregory Richard Guerin71, ORCID iconAlvaro G. Gutiérrez72, ORCID iconSylvia Haider1,2, ORCID iconMohamed Z. Hatim73,74, ORCID iconBruno Hérault75,76,77, Guillermo Hinojos Mendoza78, ORCID iconNorbert Hölzel79, ORCID iconJürgen Homeier80, Wannes Hubau81,82, Adrian Indreica83, John A.M. Janssen84, Birgit Jedrzejek79, ORCID iconAnke Jentsch85, ORCID iconNorbert Jürgens60, Zygmunt Kącki86, Jutta Kapfer87, ORCID iconDirk Nikolaus Karger88, ORCID iconAli Kavgacı89, ORCID iconElizabeth Kearsley90, ORCID iconMichael Kessler91, ORCID iconLarisa Khanina92, Timothy Killeen93, Andrey Korolyuk94, ORCID iconHolger Kreft95, ORCID iconHjalmar S. Kühl1,96, ORCID iconAnna Kuzemko97, ORCID iconFlavia Landucci6, ORCID iconAttila Lengyel98, ORCID iconFrederic Lens99,100, ORCID iconDébora Vanessa Lingner101, Hongyan Liu102, ORCID iconTatiana Lysenko103,104,105, ORCID iconMiguel D. Mahecha1,106, ORCID iconCorrado Marcenò6,34, ORCID iconVasiliy Martynenko107, ORCID iconJesper Erenskjold Moeslund108, ORCID iconAbel Monteagudo Mendoza109,110, ORCID iconLadislav Mucina111,112, ORCID iconJonas V. Müller113, ORCID iconJérôme Munzinger114, Alireza Naqinezhad115, ORCID iconJalil Noroozi116, ORCID iconArkadiusz Nowak117,118, Viktor Onyshchenko119, ORCID iconGerhard E. Overbeck120, ORCID iconMeelis Pärtel19, ORCID iconAníbal Pauchard121,122, ORCID iconRobert K. Peet123, ORCID iconJosep Peñuelas124,125, ORCID iconAaron Pérez-Haase126,127, Tomáš Peterka6, ORCID iconPetr Petřík128, ORCID iconGwendolyn Peyre129, ORCID iconOliver L. Phillips14, Vadim Prokhorov130, ORCID iconValerijus Rašomavičius131, ORCID iconRasmus Revermann132,133, ORCID iconGonzalo Rivas-Torres134, John S. Rodwell135, ORCID iconEszter Ruprecht136, ORCID iconSolvita Rūsiņa137, Cyrus Samimi138, ORCID iconMarco Schmidt139, ORCID iconFranziska Schrodt59, Hanhuai Shan140, ORCID iconPavel Shirokikh107, ORCID iconJozef Šibík141, ORCID iconUrban Šilc142, Petr Sklenář143, ORCID iconŽeljko Škvorc144, Ben Sparrow145, ORCID iconMarta Gaia Sperandii21,146, Zvjezdana Stančić147, ORCID iconJens-Christian Svenning148, Zhiyao Tang102, Cindy Q. Tang149, Ioannis Tsiripidis150, ORCID iconKim André Vanselow151, Rodolfo Vásquez Martínez109, Kiril Vassilev24, ORCID iconEduardo Vélez-Martin152, ORCID iconRoberto Venanzoni153, Alexander Christian Vibrans101, Cyrille Violle154, ORCID iconRisto Virtanen1,155,156, Henrik von Wehrden157, ORCID iconViktoria Wagner158, Donald A. Walker159, ORCID iconDonald M. Waller160, Hua-Feng Wang161, Karsten Wesche1,162,163, ORCID iconTimothy J.S. Whitfeld164, ORCID iconWolfgang Willner116, ORCID iconSusan K. Wiser5, ORCID iconThomas Wohlgemuth165, Sergey Yamalov166, ORCID iconMartin Zobel19, ORCID iconHelge Bruelheide1,2

— To whom correspondence should be addressed: francesco.sabatini@botanik.uni-halle.de
— These authors contributed equally to this work

  1. German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstraße 4, 04103, Leipzig, Germany
  2. Martin-Luther University Halle-Wittenberg, Institute of Biology, Am Kirchtor 1, 06108, Halle, Germany
  3. Université de Picardie Jules Verne, Unité de Recherche “Ecologie et Dynamique des Systèmes Anthropisés” (EDYSAN), UMR 7058 CNRS, 1 Rue des Louvels, 80000, Amiens, France
  4. MARBEC, Univ Montpellier, CNRS, IFREMER and IRD, Sète, France
  5. Manaaki Whenua - Landcare Research, PO Box 69040, 7640, Lincoln, New Zealand
  6. Masaryk University, Department of Botany and Zoology, Kotlářská 2, 611 37, Brno, Czech Republic
  7. Zurich University of Applied Sciences (ZHAW), Vegetation Ecology Group, Institute of Natural Resource Sciences (IUNR), Grüentalstr. 14, 8820, Wädenswil, Switzerland
  8. University of Bayreuth, Plant Ecology, Bayreuth Center of Ecology and Environmental Research (BayCEER), Universitätsstr. 30, 95447, Bayreuth, Germany
  9. Université de Strasbourg, Institut de biologie moléculaire des plantes-CNRS, 12, rue du Général-Zimmer, F-67084, Strasburg, France
  10. Wageningen Environmental Research, P.O.Box 47, 6700 AA, Wageningen, Netherlands
  11. University of Rostock, Faculty of Agricultural and Environmental Sciences, Justus-von-Liebig-Weg 6, 18059, Rostock, Germany
  12. University of Oviedo, Research Unit of Biodiversity (CSIC/UO/PA), C. Gonzalo Gutiérrez Quirós s/n, 33600, Mieres, Spain
  13. Max Planck Institute for Biogeochemistry, Hans Knöll Str. 10, 07745, Jena, Germany
  14. University of Leeds, School of Geography, Woodhouse Lane, LS2 9JT, Leeds, United Kingdom
  15. Universidade Federal do Rio Grande do Sul, Department of Ecology, Av. Bento Gonçalves 9500, 91501-970, Porto Alegre, RS, Brazil
  16. Medical School of the Martin-Luther University Halle-Wittenberg, Institute for Medical Epidemiology, Biometrics and Informatics (IMEBI), Interdisciplinary Center for Health Sciences, Magdeburger Straße 8, 06112, Halle/Saale, Germany
  17. Santa Clara University, Department of Biology, 500 El Camino Real, 95053, Santa Clara CA, United States
  18. Shahjalal University of Science & Technology, Forestry & Environmental Science, 3114, Sylhet, Bangladesh
  19. University of Tartu, Institute of Ecology and Earth Sciences, Lai 40, 51005, Tartu, Estonia
  20. University of Belgrade, Faculty of Agriculture, Department of Botany, Nemanjina 6, 11080, Belgrade-Zemun, Serbia
  21. Roma Tre University, Department of Sciences, V.le Marconi 446, 00146, Rome, Italy
  22. ISPRA - Italian National Institute for Environmental Protection and Research, Biodiversity Conservation Department, Via Vitaliano Brancati 60, 00144, Rome, Italy
  23. University of Bonn, Plant Nutrition, INRES, Karlrobert-Kreiten-Str., 53115, Bonn, Germany
  24. Institute of Biodiversity and Ecosystem Research, Bulgarian Academy of Sciences, Department of Plant and Fungal Diversity and Resources, Acad. Georgi Bonchev 23, 1113, Sofia, Bulgaria
  25. Shahjalal University of Science & Technology, Forestry & Environmental Science, Akhalia, 3114, Sylhet, Bangladesh
  26. Universidad Autónoma Gabriel René Moreno, Dirección de la Carrera de Biología, Santa Cruz de la Sierra, Bolivia
  27. Sapienza University of Rome, Department of Environmental Biology, P.le Aldo Moro 5, 00185, Rome, Italy
  28. Natural Resources Canada, Canadian Forest Service, Great Lakes Forestry Centre, 1219 Queen St. East, P6A 2E5, Sault Ste Marie, ON, Canada
  29. University of Illinois Urbana Champaign, Department of Computer Science, 201 North Goodwin Avenue MC 258, Urbana, IL 61801, 61801.0, Urbana, USA
  30. Ghent University, Department Green chemistry and technology, Isotope Bioscience laboratory (UGent-ISOFYS), Coupure Links 653, 9000, Ghent, Belgium
  31. Ghent University, Department Environment, Computational and Applied Vegetation Ecology (UGent-CAVELab), Coupure Links 653, 9000, Ghent, Belgium
  32. Université du Québec en Abitibi-Témiscamingue, Forest Research Institute, 445 boul. de l’Université, J9X5E4, Rouyn-Noranda, Canada
  33. University of Göttingen, Vegetation Ecology and Phytodiversity, Untere Karspüle 2, 37073, Göttingen, Germany
  34. University of the Basque Country UPV/EHU, Plant Biology and Ecology, P.O. Box 644, 48080, Bilbao, Spain
  35. University of Gothenburg, Department of Biological and Environmental Sciences, Carl Skottsbergs gata 22B, 41319, Gothenburg, Sweden
  36. Gothenburg Global Biodiversity Centre, Carl Skottsbergs gata 22B, 41319, Gothenburg, Sweden
  37. Free University of Bozen-Bolzano, Piazza Università, 5, 39100, Bolzano, Italy
  38. Institute of Ecology of the Volga River Basin, Laboratory of Phytodiversity Problem and of Phytocoenology, Komzina, 10, 445003, Toljatty, Russian Federation
  39. Southern Swedish Forest Research Centre, Swedish University of Agricultural Sciences, Sundsvägen 3, 230 53 Alnarp, Sweden
  40. Research Center of the Slovenian Academy of Sciences and Arts, Institute of Biology, Novi trg 2, 1000, Ljubljana, Slovenia
  41. University of Nova Gorica, School for viticulture and enology, Vipavska 13, 5000, Nova Gorica, Slovenia
  42. ISPRA - Italian National Institute for Environmental Protection and Research, Biodiversity Conservation Department, Via Vitaliano Brancati, 60, 00144, Roma, Italy
  43. Universidad Rey Juan Carlos, Department of Biology and Geology, Physics and Inorganic Chemistry, c/ Tulipán s/n, 29833, Móstoles, Spain
  44. Czech University of Life Sciences Prague, Department of Forest Ecology, Faculty of Forestry and Wood Sciences, Kamýcká 1176, 165 21, Praha 6 - Suchdol, Czech Republic
  45. Central Siberian Botanical Garden SB RAS, Zolotodolinskaya Str. 101, 630090, Novosibirsk, Russian Federation
  46. University of Pécs, Department of Ecology, Ifjúság u. 6., 7624, Pécs, Hungary
  47. Ss. Cyril and Methodius University, Institute of Biology, Faculty of Natural Sciences and Mathematics, Arhimedova 3, 1000, Skopje, Republic of North Macedonia
  48. Research Institute for Nature and Forest (INBO), Biotope Diversity, Havenlaan 88, bus 73, 1000, Brussels, Belgium
  49. Universidade Regional de Blumenau, Rua Antonio da Veiga, 140, Blumenau, 89030-903, Brazil
  50. University of Patras, Laboratory of Botany, Division of Plant Biology, Department of Biology, University Campus, 26504, Patras, Greece
  51. Institute of Botany, Czech Academy of Sciences, Department of Functional Ecology, Dukelska 135, 37901, Trebon, Czech Republic
  52. M.G. Kholodny Institute of Botany, National Academy of Sciences of Ukraine, Geobotany and ecology, Tereschenkivska, 1004, Kyiv, Ukraine
  53. College of Science, King Saud University, Botany and Microbiology Department, P.O. Box 2455, 11451, Riyadh, Saudi Arabia
  54. Damanhour University, Botany Department, Faculty of Science, Damanhour, Egypt
  55. University of Arizona, Ecology and Evolutionary Biology, 1041 E. Lowell St., AZ 85721, Tucson, United States
  56. Hochschule Weihenstephan-Triesdorf, University of Applied Sciences, Hans-Carl-von-Carlowitz-Platz 3, 85354, Freising, Germany
  57. Google LLC, 1600 Amphitheatre Pkwy, 94043.0, Mountain View, USA
  58. University of Minnesota - Twin Cities, USA
  59. University of Nottingham, School of Geography, University Park, NG7 2RD, Nottingham, United Kingdom
  60. University of Hamburg, Biodiversity, Ecology and Evolution of Plants, Institute for Plant Science & Microbiology, Ohnhorststr. 18, 22609, Hamburg, Germany
  61. Aix Marseille Univ, Avignon Université, CNRS, IRD, IMBE, Campus St-Jérôme Etoile, 13397, Marseille, France
  62. Universidad CEU San Pablo, Laboratorio de Botánica, P.O. Box 67, 28660, Boadilla del Monte, Madrid, Spain
  63. Universidad Privada Antonio Guillermo Urrelo, Laboratorio de Botánica, Jr. José Sabogal 913, Cajamarca, Peru
  64. Estudios Fitogeográficos del Perú, Herbario AQP, Sánchez Cerro 219, Manuel Prado, Paucarpata, Arequipa, Peru
  65. Climpact Data Science (CDS), Nova Sophia - Regus Nova, 291 rue Albert Caquot, CS 40095, 06902, Sophia Antipolis Cedex, France
  66. University of Mazandaran, Department of Plant Biology, Babolsar, Iran
  67. Instituto Multidisciplinario de Biología Vegetal (IMBIV-CONICET), Ecología vegetal y fitogeografía, Av. Vélez Sársfield 1611, 5000, Córdoba, Argentina
  68. Samara Federal research center of the Russian Academy of Sciences, Institute of Ecology of the Volga river basin of the Russian Academy of Science: Laboratory of Phytocoenology, Komzina, 10, 445003, Toljatty, Russian Federation
  69. The Arctic University Museum of Norway, UiT - The Arctic University of Norway, Tromsø, Norway
  70. University of Bergen, Department of Biological Sciences, Postbox 7803, Bergen, Norway
  71. University of Adelaide, School of Biological Sciences, North Terrace, 5005, Adelaide, Australia
  72. Universidad de Chile, Departamento de Ciencias Ambientales y Recursos Naturales Renovables, Facultad de Ciencias Agronomicas, Santa Rosa 11315, La Pintana, 8820808, Santiago, Chile
  73. Wageningen University, Plant Ecology and Nature Conservation Group - Environmental Sciences Department, P.O. Box Postbus 47, Droevendaalsesteeg 3, 6700 AA, Wageningen, Netherlands
  74. Tanta University, Botany and Microbiology Department - Faculty of Science, El Geish St., 31527, Tanta, Egypt
  75. CIRAD, UPR Forêts et Sociétés, Yamoussoukro, Ivory Coast
  76. University of Montpellier, Forêts et Sociétés, CIRAD, Montpellier, France
  77. INP-HB, Institut National Polytechnique Félix Houphouët-Boigny, Yamoussoukro, Côte d’Ivoire
  78. ASES Ecological and Sustainable Services, Pépinière d’Entreprises l’Espélidou, Parc d’Activités du Vinobre, 555 Chemin des Traverses, Lachapelle-sous-Aubenas, 07200, Aubenas, France
  79. University of Münster, Institute of Landscape Ecology, Heisenbergstr. 2, 48149, Münster, Germany
  80. University of Goettingen, Plant Ecology and Ecosystems Research, Untere Karspuele 2, 37073, Goettingen, Germany
  81. Ghent University, Department Environment, Laboratory of Wood Biology (UGent-WoodLab), Coupure Links 653, 9000, Ghent, Belgium
  82. Royal Museum for Central Africa, Service of Wood Biology, Leuvensesteenweg 13, 3080, Tervuren, Belgium
  83. Transilvania University of Brasov, Department of Silviculture, Sirul Beethoven 1, 500123, Brasov, Romania
  84. Wageningen University and Research, Wageningen Environmental Research (Alterra), P.O.Box 47, 6700 AA, Wageningen, Netherlands
  85. University of Bayreuth, Disturbance Ecology, Bayreuth Center of Ecology and Environmental Research, Universitaetsstr. 30, 95447, Bayreuth, Germany
  86. University of Wrocław, Botanical Garden, Sienkiewicza 23, 50-335, Wrocław, Poland
  87. Norwegian Institute of Bioeconomy Research, Holtvegen, 66, Tromsø, 9016, Norway
  88. Swiss Federal Institute for Forest, Snow and Landscape Research WSL , Biodiversity and Conservation Biology, Zürcherstrasse 111, 8903, Birmensdorf, Switzerland
  89. Karabuk University, Faculty of Foresty, Kilavuzlar Köyü Öte Karsi Üniversite Kampüsü Merkez, 78050, Karabuk, Turkey
  90. Ghent University, Department Environment, Computational and Applied Vegetation Ecology (UGent-CAVELab), Coupure Links 653, 9000, Gent, Belgium
  91. University of Zurich, Department of Systematic and Evolutionary Botany, Zollikerstrasse 107, 8008, Zurich, Switzerland
  92. branch of the M.V. Keldysh Institute of Applied Mathematics of Russian Academy of Sciences, Institute of Mathematical Problems of Biology of RAS, 1 Prof. Vitkevich, 142290.0, Pushchino, Russia
  93. Universidad Autonoma Gabriel Rene Moreno, Museo de Historia Natural Noel Kempff Mercado, Santa Cruz de la Sierra, Bolivia
  94. Central Siberian Botanical Garden, Siberian Branch, Russian Academy of Sciences, Geosystem Laboratory, Zolotodolinskaya str. 101, 630090, Novosibirsk, Russian Federation
  95. University of Göttingen, Department of Biodiversity, Macroecology and Biogeography, Büsgenweg 1, 37077, Göttingen, Germany
  96. Max Planck Institute for Evolutionary Anthropology, Deutscher Platz 6, 04103, Leipzig, Germany
  97. M.G. Kholodny Institute of Botany of the National Academy of Sciences of Ukraine, Department of Geobotany and Ecology, 2, Tereshchenkivska str., 01601, Kyiv, Ukraine
  98. Centre for Ecological Research, Institute of Ecology and Botany, Alkotmány u. 2-4., 2163, Vácrátót, Hungary
  99. Naturalis Biodiversity Center, Research Group Functional Traits, Darwinweg 2, 2333 CR, Leiden, The Netherlands
  100. Leiden University, Institute of Biology Leiden, Sylviusweg 72, 2333 BE, Leiden, The Netherlands
  101. Universidade Regional de Blumenau, Departamento de Engenharia Florestal, Rua São Paulo, 3250, 89030-000, Blumenau, Brazil
  102. Peking University, College of Urban and Environmental Sciences, Yiheyuan Rd. 5, 100871, Beijing, China
  103. Komarov Botanical Institute RAS, Laboratory of Vegetation Science, Prof. Popov 2, 197376, Saint-Petersburg, Russian Federation
  104. Institute of Ecology of the Volga River Basin RAS - Branch of the Samara Scientific Center RAS, Laboratory of Phytodiversity Problems, Komzin str. 10, 445003, Togliatti, Russian Federation
  105. Tobolsk complex scientific station of Ural Branch RAS, Group of Ecology of Living Organisms, Academician Yu. Osipov str. 15, 626152, Tobolsk, Russian Federation
  106. University of Leipzig, Remote Sensing Centre for Earth System Research, Talstr. 35, 04103, Leipzig, Germany
  107. Ufa Federal Scientific Center of the Russian Academy of Sciences, Ufa Institute of Biology, prospekt Oktyabrya, 69, 450054, Ufa, Russian Federation
  108. Aarhus University, Department of Bioscience, Grenaavej 14, 8410, Roende, Denmark
  109. Jardín Botánico de Missouri Oxapampa, Bolognesi Mz-E-6, Oxapampa, Pasco, Peru
  110. Universidad Nacional de San Antonio Abad del Cusco, Av. de la Cultura 733, Cusco, Peru
  111. Murdoch University, Harry Butler Institute, 90 South Street, Building 390, Murdoch 6150, Perth, Australia
  112. Stellenbosch University, Dept. of Geography & Environmental Studies, Private Bag X1, Matieland 7602, Stellenbosch, South Africa
  113. Royal Botanic Gardens, Kew, Conservation Science, Wakehurst Place, RH17 6TN, Ardingly, West Sussex, United Kingdom
  114. AMAP, Université de Montpellier, CIRAD, CNRS, INRAE, IRD, 34000, Montpellier, France
  115. University of Mazandaran, Department of Plant Biology, P.O. Box 47416-95447, Mazandaran, Iran
  116. University of Vienna, Department of Botany and Biodiversity Research, Rennweg 14, 1030, Vienna, Austria
  117. Polish Academy of Sciences, Botanical Garden - Center for Biodiversity Conservation, Prawdziwka 2, 02-950, Warsaw, Poland
  118. University of Opole, Institute of Biology, Oleska St. 52, 45-052, Opole, Polska
  119. National Academy of Sciences of Ukraine, M.G. Kholodny Institute of Botany, Tereshchenkivska 2, 01601, Kyiv, Ukraine
  120. Universidade Federal do Rio Grande do Sul, Department of Botany, Av. Bento Gonçalves 9500, 91501-970, Porto Alegre, Brazil
  121. Universidad de Concepción, Laboratorio de Invasiones Biológicas (LIB), Facultad de Ciencias Forestales, Victoria 631, 4030000, Concepción, Chile
  122. Institute of Ecology and Biodiversity (IEB), Chile
  123. University of North Carolina, Department of Biology, CB3280, South Road, 27599-3280, Chapel Hill, NC, United States
  124. CSIC, Global Ecology Unit CSIC-CREAF-UAB, Edifici C, Campus UAB, 08193, Bellaterra, Catalonia, Spain
  125. CREAF, Edifici C, 08193, Cerdanyola del Valles, Catalonia, Spain
  126. University of Vic-Central University of Catalonia, Department of Biosciences, Carrer de la Laura, 13, 08500, Vic, Barcelona, Spain
  127. University of Barcelona, Department of Evolutionary Biology, Ecology and Environmental Sciences, Diagonal 643, 08028, Barcelona, Spain
  128. Czech Academy of Sciences, Department of vegetation ecology, Institute of Botany, Zámek 1, 25243, Průhonice, Czech Republic
  129. University of the Andes, Department of Civil and Environmental Engineering, Carrera 1 Este No. 19A-40, Edificio Mario Laserna, Piso 6 , 111711, Bogota, Colombia
  130. Kazan Federal University, Institute of Environmental Sciences, Kremlevskaya 18, 420008, Kazan, Russian Federation
  131. Nature Research Centre, Institute of Botany, Zaliuju Ezeru 49, 08406, Vilnius, Lithuania
  132. University of Hamburg, Biodiversity, Ecology and Evolution of Plants/Institute for Plant Science & Microbiology, Ohnhorststr. 18, 22609, Hamburg, Germany
  133. Namibia University of Science and Technology, Faculty of Natural Resources and Spatial Sciences, Windhoek, Namibia
  134. Universidad San Francisco de Quito, COCIBA, Diego de Robles, 170177, Quito, Ecuador
  135. 7 Derwent Road, LA1 3ES, Lancaster, United Kingdom
  136. Babeș-Bolyai University, Hungarian Department of Biology and Ecology, Faculty of Biology and Geology, Republicii street 42., 400015, Cluj-Napoca, Romania
  137. University of Latvia, Faculty of Geography and Earth Sciences, Jelgavas iela 1, LV 1004, Riga, Latvia
  138. University of Bayreuth, Climatology, Bayreuth Center of Ecology and Environmental Research (BayCEER), Universitätsstr. 30, 95447, Bayreuth, Germany
  139. Stadt Frankfurt am Main - Der Magistrat, Palmengarten, Siesmayerstraße 61, 60323, Frankfurt am Main, Germany
  140. Microsoft, One Microsoft Way, 98052.0, Redmond, WA, United States
  141. Plant Science and Biodiversity Centre Slovak Academy of Sciences, Institute of Botany, Dubravska cesta 9, 84523, Bratislava, Slovakia
  142. Research Centre of Slovenian Academy of Sciences and Arts (ZRC SAZU), Institute of Biology, Novi trg 2, 1000, Ljubljana, Slovenia
  143. Department of Botany, Charles University, Benatska 2, 12801 Prague, Czech Republic
  144. University of Zagreb, Faculty of Forestry and Wood Technology, Svetošimunska 25, 10000, Zagreb, Croatia
  145. University of Adelaide, TERN, North Terrace, 5005, Adelaide, Australia
  146. CSIC‐UV‐GV, Centro de Investigaciones sobre Desertificación, Carretera Moncada‒Náquera km 4.5, 46113.0, Moncada (Valencia), Spain
  147. University of Zagreb, Faculty of Geotechnical Engineering, Hallerova aleja 7, 42000, Varaždin, Croatia
  148. Aarhus University, Department of Biology, Ny Munkegade 114, DK-8000, Aarhus C, Denmark
  149. Yunnan University, School of Ecology and Environmental Science, Building Shixun, Chenggong Campus, Dongwaihuan South Road, University Town, Chenggong New District, 650504, Kunming, China
  150. Aristotle University of Thessaloniki, School of Biology, 54124, Thessaloniki, Greece
  151. University of Erlangen-Nuremberg, Department of Geography, Wetterkreuz 15, 91058, Erlangen, Germany
  152. ILEX Consultoria Científica, Amelia Telles 184, 90.460-070, Porto Alegre, Brazil
  153. University of Perugia, Department of Chemistry, Biology and Biotechnology, Borgo XX giugno 74, 06124, Perugia, Italy
  154. Univ Montpellier, CNRS, EPHE, IRD, Univ Paul Valéry Montpellier 3, CEFE, 1919 route de Mende, 34293, Montpellier, France
  155. University of Oulu, Ecology and Genetics Research Unit, Biodiversity Unit, Kaitoväylä 5, 90014, Oulu, Finland
  156. Helmholtz Center for Environmental Research - UFZ, Department of Physiological Diversity, Permoserstr. 15, 04318, Leipzig, Germany
  157. Leuphana University of Lüneburg, Institute of Ecology, Universitätsallee 1, 21335, Lüneburg, Germany
  158. University of Alberta, Department of Biological Sciences, Biological Sciences Building, T6G2E9, Edmonton, Canada
  159. University of Alaska, Institute of Arctic Biology, P. O. Box 7570000, 99775, Fairbanks, United States
  160. University of Wisconsin-Madison, Botany, 430 Lincoln Drive, 53706, Madison, United States
  161. Hainan University, Hainan Key Laboratory for Sustainable Utilization of Tropical Bioresources, College of Tropical Crops, 58 Renmin Avenue, Meilan District, 570228, Haikou, China
  162. Senckenberg Museum of Natural History Görlitz, Botany Department, PO Box 300 154, 02806, Görlitz, Germany
  163. Technische Universität Dresden, International Institute Zittau, Markt 23, 02763, Zittau, Germany
  164. University of Minnesota, Bell Museum, 1445 Gortner Avenue, 55108.0, St. Paul, USA
  165. Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Forest Dynamics, Zürcherstrasse 111, CH-8909, Birmensdorf, Switzerland
  166. Ufa Scientific Centre, Russian Academy of Sciences, Laboratory of Wild-Growing Flora, South-Ural Botanical Garden-Institute, Mendeleev str., 195/3, 450080, Ufa, Russian Federation

Short Running Title

sPlotOpen: a global vegetation plot database

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, an 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 is not open-access. Here, we address both these issues by (a) resampling the vegetation plots using several environmental variables as sampling strata (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 patterns of 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 occurring vascular plant species within delimited areas. sPlotOpen contains three partially overlapping resampled datasets (~50,000 plots each), to be used as replicates in global analyses. Besides geographic 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’ 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

Biodiversity, Biogeography, Big-data, Database, Functional traits, Macroecology, Vascular plants, Vegetation plots

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 (1, 2). In addition, the rates of biodiversity homogenization and redistribution are accelerating (3, 4; 5). 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 (1; 5). Many terrestrial and marine species are also shifting their geographical distribution as a response to climate change (4). This has profound potential impacts on ecosystems and human health (6; 7).

Plant communities are no exception to this biodiversity crisis (8; 9; 5). This is particularly worrying since terrestrial vegetation accounts for 80% (450 Gt C) of the living biomass on Earth (10). Given the central role of vegetation in ecosystem productivity, structure, stability and functioning (9), 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 spatiotemporal scales (11, 12). Large independent collections of plant occurrence data do exist at the global or continental extent via the Botanical Information and Ecology Network (BIEN) (13), the Global Inventory of Floras and Traits (GIFT) (14) or the Global Biodiversity Information Facility (GBIF) (https://www.gbif.org/). However, these databases suffer from one or several of the following limitations: (1) imbalance towards tree species only; (2) lack of data on how individual plant species co-occur and interact locally to form plant communities; or (3) coarse spatial resolutions (e.g., one‐degree grid cells), which preclude intersection with high resolution remote sensing data and the assessment of biodiversity trends at the plant community level (15).

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 ‘quadrat’) of a given size (i.e. surface area) at a given time (e.g. 16). Compared to presence-only data, vegetation-plot data present many advantages. As all visible plant species are recorded, plots contain information on which plant species do, and do not co‐occur in the same locality at a given moment in time (17). This is important for testing hypotheses related to biotic interactions among plant species. Vegetation-plot data also provide crucial information on where and when a species was absent, therefore improving predictions from current species distribution models (18). Being spatially explicit, vegetation plots can be resurveyed through time to assess potential changes in plant species composition relative to a baseline (19; 20, 5). As they normally contain 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 (21, 22).

Globally, however, vegetation-plot data are very fragmented, as they typically stem from a myriad of local research and survey projects (23). These are fine-grained data (e.g., 1-10,000 m2) normally covering small spatial extents (e.g., 1-1,000 km2)(24). With their disparate sampling protocols, standards and taxonomic resolutions, aggregating and harmonizing vegetation plot data proves extremely challenging (25). It is not surprising, therefore, that these data are rarely used in global‐scale research on the biodiversity of plant communities (26; 27; 28).

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 (23). Established in 2013, sPlot (version 3) currently contains more than 1.9 million vegetation plots, and is fully integrated with the TRY database (29), from which it derives information on plant functional traits. The sPlot database is increasingly being used to study continental-to-global scale vegetation patterns (30, 31), such as the relative contribution of regional vs. local factors on the global patterns of fern richness (32), the mechanisms underlying the spread and abundance of native vs. invasive tree species (33), and worldwide trait–environment relationships in plant communities (25).

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 release using a replicated environmental stratification, in order to represent the entire environmental space covered by the sPlot database. This maximises 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: (1) plot-level information, including metadata and basic vegetation structure descriptors; (2) the vascular plant species composition of each vegetation plot, including species cover or abundance information when available; and (3) community-level functional information obtained by intersection with the TRY database (29).

sPlotOpen is specifically designed for global macroecological studies, e.g., the exploration of functional diversity patterns at the plant community scale with continental-to-global extent. We expect, however, 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 vs. 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 communities 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 vegetation plots, varying plot size, and nested vegetation plots.

Figure 1: Top: Global distribution of all vegetation plots contained in sPlotOpen (n = 95,104). Each color represents a different source dataset (n = 105 - different datasets might have the same color). 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.

Methods

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 refers to natural and semi-natural vegetation, while vegetation shaped by intensive and repeated 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 stemming from regional or continental initiatives (see 23 for more information). For instance: 48 vegetation-plot datasets derive from the European Vegetation Archive (EVA) (17); 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 (34; 35) and TERN’s AEKOS (36) 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 (37), using the GIVD code as the unique dataset identifier.

Resampling method

Data in the sPlot database are unevenly distributed across vegetation types and geographic regions (see 25). 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 (e.g., 38; 4). To reduce this imbalance as much as possible, we performed a stratified resampling approach, 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 few geographic 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 v1.2 (39), 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 average temperatures. Specifically, we summed the number of days of those months with average temperature greater than 1 °C or 5 °C, respectively. In addition, we considered an index of aridity and a layer for potential evapotranspiration from the Consortium of Spatial Information (CGIAR-CSI 40). For soil, we extracted seven variables from the SoilGrids database (41), namely: (1) soil organic carbon content in the fine earth fraction; (2) cation exchange capacity; (3) pH; as well as the fractions of (4) coarse fragments; (5) sand; (6) silt; and (7) clay.
The results of this PCA represents the full environmental space of all terrestrial habitats on Earth, irrespective of whether a grid cell hosted vegetation plots or not (Figure S1). We then subdivided the PCA ordination space, represented by the first two principal components (PC1–PC2), which accounted for 47% and 23% 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 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 much balanced as possible across the PC1-PC2 environmental space. To select these 50 vegetation plots we used the heterogeneity-constrained random resampling algorithm from Lengyel et al. (2011) [42]. 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 when resampling vegetation plots. We quantified the variability in plant species composition among the 50 randomly selected vegetation plots by computing the mean and the variance of the Jaccard’s dissimilarity index (43) between all possible pairs of these 50 vegetation plots (n = 1,225). 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 less 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. 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 different iterations. In addition, those plots located in PC1-PC2 grid cells with less than 50 vegetation plots are completely shared by all three iterations.

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 (gray background) in the PCA space. Only the plots in the environmentally-balanced dataset selected by 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 axis explained 47% and 23% of the total variance. Bottom right: Geographic distribution of the vegetation plots contained in four randomly selected grid cells.

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 iteration #1, #2 and #3, respectively, for a total 107,238 unique vegetation plots. Since the sPlot database is a consortium of independent datasets whose copyright belongs to the data contributor, 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 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 iteration #1, #2 and #3 were 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 iteration, 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, i.e., the same PC1-PC2 grid cell, of the confidential vegetation plot, even if 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).

Trait information

For each vegetation plot for which open access could be granted, we computed the community-weighted mean and variance for eighteen plant functional traits derived from the TRY database v3.0 (29). These traits were selected among those that describe the leaf, wood and seed economics spectra (44; 45), and are known to either affect different key ecosystem processes or respond to macroclimatic drivers, or both (23). The eighteen plant functional traits (all concentrations based on dry weight) were: (1) leaf area [mm2]; (2) stem specific density [g cm-3]; (3) specific leaf area [m2kg-1]; (4) leaf carbon concentration [mg g-1]; (5) leaf nitrogen concentration [mg g-1]; (6) leaf phosphorus concentration [mg g-1]; (7) plant height [m]; (8) seed mass [mg]; (9) seed length [mm]; (10) leaf dry matter content [g g-1]; (11) leaf nitrogen per area [g m-2]; (12) leaf N:P ratio [g g-1]; (13) leaf 𝛿15N [per million]; (14) seed number per reproductive unit; (15) leaf fresh mass [g]; (16) stem conduit density [mm-2]; (17) dispersal unit length [mm]; and (18) 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 (29). Gap-filling was performed at the level of individual observations and relies on a hierarchical Bayesian modeling (R package ‘BHPMF’, 46; 47). 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, i.e. 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 [23].

Community‐weighted means (CWM) and variances (CWV) were calculated for every plant functional trait j and every vegetation plot k as follows (48):

\[ CWM_{j,k} = \sum_{i}^{n_k} p_{i,k} t_{i,j}\](1)
\[ CWV_{j,k} = \sum_{i}^{n_k} p_{i,k} (t_{i,j} - CWM_{j,k})^2\](2)

where nk is the number of species with trait information in vegetation plot k, pi,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 ti,j is the mean value of species i for trait j.

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 not granted as open access 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) information on the exact group of plants sampled in the field is 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 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, i.e., 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 or biomes. 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. 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.

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 color coded based on sBiomes, i.e., sPlot’s definition of biomes (23), which derives from Schultz (2005)(49) ecozones, modified to include also the alpine biome from Körner et al. (2017)(50). Right: the same plots superimposed onto Whittaker (1975) biomes (51), as adapted by Rickleff (2008)(52) and plotted using the R package plotbiomes.

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, in 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) [23]. Even if the proportion of forest vs. 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 description of each individual dataset in GIVD and to contact the custodians of each dataset for further information.

Database Organization

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, ownership, 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 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 EUNIS habitat classification (column ‘ESY’), based on the habitat classification expert system described in Chytrý et al. (2020) [53]. For each vegetation plot, we further provide information on the dataset it originates from, based on the IDs used in GIVD. 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_species’), and the taxon name after taxonomic standardization (column ‘Species’). For details on the taxonomic standardization, please see ‘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, i.e., number of individuals in plot; ‘x_SC’ = stem count, i.e., number of stems in plot; ‘x_IV’ = importance value index; ‘x_PF’ = 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’, i.e., 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 column ‘Species_richness’ shows the number of species recorded in each plot. The columns ‘Trait_coverage_cover’ and ‘Trait_coverage_pa’ 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 0.85 (median = 0.95). 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 0.87, 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’ - 37), 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 collected. 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_plotID’, ‘Original_subplotID’). The last two columns report plot-level ‘Remarks’, and the unique identifier produced by Turboveg when the vegetation plot was first stored (‘GUID’).

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 an .RData object.

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 (54). Turboveg is a program specifically designed for the storage, selection and export of vegetation plots (https://www.synbiosys.alterra.nl/turboveg/). During this conversion, we checked that all datasets contained the required metadata information, and cross-checked that each plot was located within the geographic scopes 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 the sPlot’s taxonomic backbone (55). This backbone matched all the taxonomic names (without nomenclatural authors) from all datasets in sPlot 2.1 and TRY v3.0 (29) to their resolved version based on the Taxonomic Name Resolution Service web application (TNRS version 4.0; 56). This allowed us to (1) harmonize all datasets to a common nomenclature, and (2) link the sPlot database to the TRY database (29). The final backbone only retained matched taxonomic names at the rank of species or higher. Additional detail on the taxonomic resolution is reported in [23], while a description of the workflow, including R‐code, is available in [55].

Usage Notes

The sPlotOpen database can be downloaded from https://doi.org/10.25829/idiv.3474-40-3292. Users are urged to cite the original sources when using sPlotOpen in addition to the present paper, particularly when using data contained in BioTIME (57). 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 (custodian names are reported in https://www.idiv.de/en/splot).

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 distribution of plant communities worldwide. Second, for most regions data was 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, which should be accounted for when comparing biodiversity indices (e.g., species richness, beta-diversity) across plots or regions. Fourth, not all plots contain complete information on all plant species. A limited number of plots, mostly located in tropical regions, only contain data on woody species. This should be kept in mind when exploring biodiversity patterns. Finally, a small fraction of plots represent 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 problems depends on the problem being analyzed. Users are therefore invited to carefully consider the limitations above when designing applications relying on sPlotOpen.

The data included in the present paper represent the subset of sPlot for which we were able to secure permission for making these data open. The additional data in 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.

Code Availability

The R code used to produce sPlotOpen from the sPlot 2.1 database is contained in the sPlotOpen_code GitHub repository: https://github.com/fmsabatini/sPlotOpen_Code. A short interactive vignette introducing to the use of sPlotOpen is in Appendix 1. This manuscript was produced using the Manubot workflow (58). The code for reproducing this manuscript is stored in the sPlotOpen_manuscript GitHub repository: https://github.com/fmsabatini/sPlotOpen_Manuscript.

Acknowledgements

We are grateful to thousands of vegetation scientists who sampled vegetation plots in the field or digitized them into regional, national or international databases. We 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. We acknowledge this support with naming the database “sPlot”, where the “s” refers to the sDiv synthesis workshops. We 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 the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig. Jens Kattge acknowledges support by the Max Planck Institute for Biogeochemistry (Jena, Germany), Future Earth, the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig and the EU H2020 project BACI, Grant No 640176.

Isabelle Aubin was funded through Natural Sciences and Engineering Research Council of Canada and Ontario Ministry of Natural Resources and Forestry. Yves Bergeron was funded through 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. 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 supervision of the MSc thesis, 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 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). Larisa Khanina was supported by the Ministry of Science and Higher Education of the Russian Federation (project no. AAAA-A19-119012490096-2). Hjalmar Kühl gratefully acknowledges the Pan African team and funding by 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 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), BIONEOCAL (ANR-07-BDIV-0006) & ULTRABIO (ANR-07-BDIV-0010), by National Geographic Society (Grant 7579-04), and with fundings 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 has been supported by the Brazil’s National Council of Scientific and Technological Development (CNPq, grant 307689/2014-0). Solvita Rūsiņa was supported by the University of Latvia grant AAP2016/B041//Zd2016/AZ03 within the “Climate change and sustainable use of natural resources”. 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 nr. 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 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.

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. JK provided the trait data from TRY and FS 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.

Competing interests

The authors declare no competing interests.

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 became established as the largest vegetation-plot databases 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 are the exploration of the relationships between environmental drivers, trait variation, and assembly processes in local plant communities worldwide.

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DOI: 10.1111/j.1654-1103.2011.01312.x

60. Plot-scale evidence of tundra vegetation change and links to recent summer warming
Sarah C. Elmendorf, Gregory H. R. Henry, Robert D. Hollister, Robert G. Björk, Noémie Boulanger-Lapointe, Elisabeth J. Cooper, Johannes H. C. Cornelissen, Thomas A. Day, Ellen Dorrepaal, Tatiana G. Elumeeva, … Sonja Wipf
Nature Climate Change (2012-04-08) https://doi.org/f223nb
DOI: 10.1038/nclimate1465

61. Database of Masaryk University’s Vegetation Research in Siberia
Milan Chytrý
Biodiversity & Ecology (2012-09-10) https://doi.org/ghgvcp
DOI: 10.7809/b-e.00088

62. The West African Vegetation Database
Marco Schmidt, Thomas Janßen, Stefan Dressler, Karen Hahn, Mipro Hien, Souleymane Konaté, Anne Mette Lykke, Ali Mahamane, Bienvenu Sambou, Brice Sinsin, … Georg Zizka
Biodiversity & Ecology (2012-09-10) https://doi.org/ghgvcf
DOI: 10.7809/b-e.00065

63. BIOTA Southern Africa Biodiversity Observatories Vegetation Database
Gerhard Muche, Ute Schmiedel, Norbert Jürgens
Biodiversity & Ecology (2012-09-10) https://doi.org/ghgvcg
DOI: 10.7809/b-e.00066

64. Vegetation Database of the Okavango Basin
Rasmus Revermann, Amândio Luis Gomes, Francisco Maiato Gonçalves, Johannes Wallenfang, Torsten Hoche, Norbert Jürgens, Manfred Finckh
Phytocoenologia (2016-06-01) https://doi.org/ghgt82
DOI: 10.1127/phyto/2016/0103

65. Zur Vegetationsökologie der Savannenlandschaften im Sahel Burkina Fasos
J. Müller
FB Biologie und Informatik, J.W. Goethe‐Universität Frankfurt a.M (2003)

66. Conventional tree height–diameter relationships significantly overestimate aboveground carbon stocks in the Central Congo Basin
Elizabeth Kearsley, Thales de Haulleville, Koen Hufkens, Alidé Kidimbu, Benjamin Toirambe, Geert Baert, Dries Huygens, Yodit Kebede, Pierre Defourny, Jan Bogaert, … Hans Verbeeck
Nature Communications (2013-08-05) https://doi.org/ghgt8w
DOI: 10.1038/ncomms3269 · PMID: 23912554

67. Responses of plant functional types to environmental gradients in the south-west Ethiopian highlands
Desalegn Wana, Carl Beierkuhnlein
Journal of Tropical Ecology (2011-03-10) https://doi.org/b6mtmx
DOI: 10.1017/s0266467410000799

68. Vegetation Database of Southern Morocco
Manfred Finckh
Biodiversity & Ecology (2012-09-10) https://doi.org/ghgvcq
DOI: 10.7809/b-e.00094

69. {Das Weidepotential im Gutu‐Distrikt (Zimbabwe) – Möglichkeiten und Grenzen der Modellierung unter Verwendung von Landsat TM‐5
C. Samimi
Karlsruher Schriften zur Geographie und Geoökologie (2003)

70. Classification of Korean forests: patterns along geographic and environmental gradients
Tomáš Černý, Martin Kopecký, Petr Petřík, Jong-Suk Song, Miroslav Šrůtek, Milan Valachovič, Jan Altman, Jiří Doležal
Applied Vegetation Science (2015-01) https://doi.org/ghgt8z
DOI: 10.1111/avsc.12124

71. Vegetation of Middle Asia – the project state of art after ten years of survey and future perspectives
Arkadiusz Nowak, Marcin Nobis, Sylwia Nowak, Agnieszka Nobis, Grzegorz Swacha, Zygmunt Kącki
Phytocoenologia (2017-12-01) https://doi.org/gctffg
DOI: 10.1127/phyto/2017/0208

72. Vegetation of the woodland-steppe transition at the southeastern edge of the Inner Mongolian Plateau
Hongyan Liu, Haiting Cui, Richard Pott, Martin Speier
Journal of Vegetation Science (2000-08) https://doi.org/cxr92b
DOI: 10.2307/3246582

73. Combined effects of livestock grazing and abiotic environment on vegetation and soils of grasslands across Tibet
Yun Wang, Gwendolyn Heberling, Eugen Görzen, Georg Miehe, Elke Seeber, Karsten Wesche
Applied Vegetation Science (2017-07) https://doi.org/gbkd6v
DOI: 10.1111/avsc.12312

74. Community assembly during secondary forest succession in a Chinese subtropical forest
Helge Bruelheide, Martin Böhnke, Sabine Both, Teng Fang, Thorsten Assmann, Martin Baruffol, Jürgen Bauhus, François Buscot, Xiao-Yong Chen, Bing-Yang Ding, … Bernhard Schmid
Ecological Monographs (2011-02) https://doi.org/dmwpsm
DOI: 10.1890/09-2172.1

75. Vegetation Database of Sinai in Egypt
Mohamed Hatim
Biodiversity & Ecology (2012-09-10) https://doi.org/ghgvcr
DOI: 10.7809/b-e.00099

76. Eurosiberian meadows at their southern edge: patterns and phytogeography in the NW Tien Shan
Viktoria Wagner
Journal of Vegetation Science (2009-03-25) https://doi.org/ftq2r6
DOI: 10.1111/j.1654-1103.2009.01032.x

77. Plant communities of the southern Mongolian Gobi
Henrik von Wehrden, Karsten Wesche, Georg Miehe
Phytocoenologia (2009-10-21) https://doi.org/ddvj9h
DOI: 10.1127/0340-269x/2009/0039-0331

78. Wetland Vegetation Database of Baikal Siberia (WETBS)
Victor Chepinoga
Biodiversity & Ecology (2012-09-10) https://doi.org/ghgvcs
DOI: 10.7809/b-e.00107

79. Database of Siberian Vegetation (DSV)
Andrei Zverev, Andrey Korolyuk
Biodiversity & Ecology (2012-09-10) https://doi.org/ghmxn2
DOI: 10.7809/b-e.00108

80. SaudiVeg ecoinformatics: Aims, current status and perspectives
Mohamed A. El-Sheikh, Jacob Thomas, Ahmed H. Alfarhan, Abdulrahman A. Alatar, Sivadasan Mayandy, Stephan M. Hennekens, Joop H. J. Schaminėe, Ladislav Mucina, Abdulla M. Alansari
Saudi Journal of Biological Sciences (2017-02) https://doi.org/ghmwh5
DOI: 10.1016/j.sjbs.2016.02.012 · PMID: 28149178 · PMCID: PMC5272952

81. Eastern Pamirs – A vegetation-plot database for the high mountain pastures of the Pamir Plateau (Tajikistan)
Kim André Vanselow
Phytocoenologia (2016-06-01) https://doi.org/f952sp
DOI: 10.1127/phyto/2016/0122

82. Socotra Vegetation Database
Michele De Sanctis, Fabio Attorre
Biodiversity & Ecology (2012-09-10) https://doi.org/ghgvct
DOI: 10.7809/b-e.00111

83. Structural and floristic diversity of mixed tropical rain forest in New Caledonia: new data from the New Caledonian Plant Inventory and Permanent Plot Network (NC-PIPPN)
Thomas Ibanez, Jérôme Munzinger, Gilles Dagostini, Vanessa Hequet, Frédéric Rigault, Tanguy Jaffré, Philippe Birnbaum
Applied Vegetation Science (2014-07) https://doi.org/f57bfw
DOI: 10.1111/avsc.12070

84. Managing biodiversity information: development of New Zealand’s National Vegetation Survey databank
S. K. Wiser, P. J. Bellingham, L. E. Burrows
New Zealand Journal of Ecology (2001) https://www.jstor.org/stable/24055293

85. Species Richness, Forest Structure, and Functional Diversity During Succession in the New Guinea Lowlands
Timothy J. S. Whitfeld, Jesse R. Lasky, Kipiro Damas, Gibson Sosanika, Kenneth Molem, Rebecca A. Montgomery
Biotropica (2014-09) https://doi.org/f6hf36
DOI: 10.1111/btp.12136

86. Database Dry Grasslands in the Nordic and Baltic Region
Jürgen Dengler, Solvita Rūsiņa
Biodiversity & Ecology (2012-09-10) https://doi.org/ghgvcv
DOI: 10.7809/b-e.00114

87. Vegetation-Plot Database of the University of the Basque Country (BIOVEG)
Idoia Biurrun, Itziar García-Mijangos, Juan Campos, Mercedes Herrera, Javier Loidi
Biodiversity & Ecology (2012-09-10) https://doi.org/ghgt9d
DOI: 10.7809/b-e.00121

88. Balkan Dry Grasslands Database
Kiril Vassilev, Zora Dajiś, Renata Cušterevska, Erwin Bergmeier, Iva Apostolova
Biodiversity & Ecology (2012-09-10) https://doi.org/ghgvcw
DOI: 10.7809/b-e.00123

89. The Mediterranean Ammophiletea Database: a comprehensive dataset of coastal dune vegetation
Corrado Marcenò, Borja Jiménez-Alfaro
Phytocoenologia (2016) https://doi.org/ghgt83
DOI: 10.1127/phyto/2016/0133

90. Local temperatures inferred from plant communities suggest strong spatial buffering of climate warming across Northern Europe
Jonathan Lenoir, Bente Jessen Graae, Per Arild Aarrestad, Inger Greve Alsos, W. Scott Armbruster, Gunnar Austrheim, Claes Bergendorff, H. John B. Birks, Kari Anne Bråthen, Jörg Brunet, … Jens-Christian Svenning
Global Change Biology (2013-05) https://doi.org/f24bdd
DOI: 10.1111/gcb.12129 · PMID: 23504984

91. Balkan Vegetation Database: historical background, current status and future perspectives
Kiril Vassilev, Hristo Pedashenko, Alexandra Alexandrova, Alexandar Tashev, Anna Ganeva, Anna Gavrilova, Asya Gradevska, Assen Assenov, Antonina Vitkova, Borislav Grigorov, … Vladimir Vulchev
Phytocoenologia (2016-06-01) https://doi.org/f8sjft
DOI: 10.1127/phyto/2016/0109

92. WetVegEurope: a database of aquatic and wetland vegetation of Europe
Flavia Landucci, Marcela Řezníčková, Kateřina Šumberová, Milan Chytrý, Liene Aunina, Claudia Biţă-Nicolae, Alexander Bobrov, Lyubov Borsukevych, Henry Brisse, Andraž Čarni, … Wolfgang Willner
Phytocoenologia (2015-07-01) https://doi.org/bdmw
DOI: 10.1127/phyto/2015/0050

93. European Mire Vegetation Database: a gap-oriented database for European fens and bogs
Tomáš Peterka, Martin Jiroušek, Michal Hájek, Borja Jiménez-Alfaro
Phytocoenologia (2015-11-01) https://doi.org/f724p4
DOI: 10.1127/phyto/2015/0054

94. Vegetation Database of Albania
Michele De Sanctis, Giuliano Fanelli, Alfred Mullaj, Fabio Attorre

Phytocoenologia (2017-01-01) https://doi.org/ghgt85
DOI: 10.1127/phyto/2017/0178

95. Austrian Vegetation Database
Wolfgang Willner, Christian Berg, Paul Heiselmayer
Biodiversity & Ecology (2012-09-10) https://doi.org/ghgvcx
DOI: 10.7809/b-e.00125

96. Bulgarian Vegetation Database: historic background, current status and future prospects
Iva Apostolova, Desislava Sopotlieva, Hristo Pedashenko, Nikolay Velev, Kiril Vasilev
Biodiversity & Ecology (2012-09-10) https://doi.org/ghgvch
DOI: 10.7809/b-e.00069

97. Swiss Forest Vegetation Database
Thomas Wohlgemuth
Biodiversity & Ecology (2012-09-10) https://doi.org/ghgvcz
DOI: 10.7809/b-e.00131

98. Czech National Phytosociological Database: basic statistics of the available vegetation‐plot data
M. Chytrý, M. Rafajová
Preslia (2003)

99. VegMV – the vegetation database of Mecklenburg-Vorpommern
Florian Jansen, Jürgen Dengler, Christian Berg
Biodiversity & Ecology (2012-09-10) https://doi.org/gftw54
DOI: 10.7809/b-e.00070

100. VegetWeb – the national online-repository of vegetation plots from Germany
Jörg Ewald, Rudolf May, Martin Kleikamp
Biodiversity & Ecology (2012-09-10) https://doi.org/ghgvcj
DOI: 10.7809/b-e.00073

101. German Vegetation Reference Database (GVRD)
Ute Jandt, Helge Bruelheide
Biodiversity & Ecology (2012-09-10) https://doi.org/ghgvc2
DOI: 10.7809/b-e.00146

102. The phytosociological database SOPHY as the basis of plant socio-ecology and phytoclimatology in France
Emmanuel Garbolino, Patrice De Ruffray, Henry Brisse, Gilles Grandjouan
Biodiversity & Ecology (2012-09-10) https://doi.org/ghhn9q
DOI: 10.7809/b-e.00074

103. Hellenic Natura 2000 Vegetation Database (HelNatVeg)
Panayotis Dimopoulos, Ioannis Tsiripidis
Biodiversity & Ecology (2012-09-10) https://doi.org/ghgvc3
DOI: 10.7809/b-e.00177

104. Hellenic Woodland Database
Georgios Fotiadis, Ioannis Tsiripidis, Erwin Bergmeier, Panayotis Dimopolous
Biodiversity & Ecology (2012-09-10) https://doi.org/ghgvc4
DOI: 10.7809/b-e.00178

105. Phytosociological Database of Non-Forest Vegetation in Croatia
Zvjezdana Stancic
Biodiversity & Ecology (2012-09-10) https://doi.org/ghgt9f
DOI: 10.7809/b-e.00180

106. Hungarian Phytosociological database (COENODATREF): sampling methodology, nomenclature and its actual stage
K Lájer, Z. Botta‐Dukát, J. Csiky, F. Horváth, F. Szmorad, I. Bagi, T. Rédei
Annali di Botanica, Nuova Serie (2008)

107. VegItaly: The Italian collaborative project for a national vegetation database
F. Landucci, A. T. R. Acosta, E. Agrillo, F. Attorre, E. Biondi, V. E. Cambria, A. Chiarucci, E. Del Vico, M. De Sanctis, L. Facioni, … R. Venanzoni
Plant Biosystems - An International Journal Dealing with all Aspects of Plant Biology (2012-12) https://doi.org/ghgt8x
DOI: 10.1080/11263504.2012.740093

108. Italian National Vegetation Database (BVN/ISPRA)
Laura Casella, Pietro Massimiliano Bianco, Pierangela Angelini, Emi Morroni
Biodiversity & Ecology (2012-09-10) https://doi.org/ghgvc6
DOI: 10.7809/b-e.00192

109. Nationwide Vegetation Plot Database – Sapienza University of Rome: state of the art, basic figures and future perspectives
Emiliano Agrillo*, Nicola Alessi, Marco Massimi, Francesco Spada, Michele De Sanctis
Phytocoenologia (2017-07-20) https://doi.org/gbsxm9
DOI: 10.1127/phyto/2017/0139

110. Semi-natural Grassland Vegetation Database of Latvia
Solvita Rūsiņa
Biodiversity & Ecology (2012-09-10) https://doi.org/ghgt9g
DOI: 10.7809/b-e.00197

111. Schatten voor de natuur. Achtergronden, inventaris en toepassingen van de Landelijke Vegetatie Databank
J. H. J. Schaminée, J. A. M. Janssen, R. Haveman, S. M. Hennekens, G. B. M. Heuvelink, H. P. J. Huiskes, E. J. Weeda
KNNV Uitgeverij (2006)

112. The Polish Vegetation Database: structure, resources and development
Zygmunt Kącki, Michał Śliwiński
Acta Societatis Botanicorum Poloniae (2012) https://doi.org/f34f3k
DOI: 10.5586/asbp.2012.014

113. Romanian Forest Database: a phytosociological archive of woody vegetation
Adrian Indreica, Pavel Dan Turtureanu, Anna Szabó, Irina Irimia

Phytocoenologia (2017-12-01) https://doi.org/ghgt86
DOI: 10.1127/phyto/2017/0201

114. The Romanian Grassland Database (RGD): historical background, current status and future perspectives
Kiril Vassilev, Eszter Ruprecht, Valeriu Alexiu, Thomas Becker, Monica Beldean, Claudia Biță-Nicolae, Anna Mária Csergő, Iliana Dzhovanova, Eva Filipova, József Pál Frink, … Jürgen Dengler
Phytocoenologia (2018-03-01) https://doi.org/gc79hp
DOI: 10.1127/phyto/2017/0229

115. Vegetation Database Grassland Vegetation of Serbia
Svetlana Aćić, Milicia Petrović, Urban Šilc, Zora Dajić Stevanović
Biodiversity & Ecology (2012-09-10) https://doi.org/ghgt9h
DOI: 10.7809/b-e.00206

116. Lower Volga Valley Phytosociological Database
Alexey Sorokin, Valentin Golub, Kseniya Starichkova, Lyudmila Nikolaychuk, Viktoria Bondareva, Tatyana Ivakhnova
Biodiversity & Ecology (2012-09-10) https://doi.org/ghgt9j
DOI: 10.7809/b-e.00207

117. Vegetation Database of the Volga and the Ural Rivers Basins
Tatiana Lysenko, Olga Kalmykova, Anna Mitroshenkova
Biodiversity & Ecology (2012-09-10) https://doi.org/ghgvc7
DOI: 10.7809/b-e.00208

118. Vegetation Database of Tatarstan
Vadim Prokhorov, Tatiana Rogova, Maria Kozhevnikova
Phytocoenologia (2017-09-27) https://doi.org/ghgt84
DOI: 10.1127/phyto/2017/0172

119. Vegetation Database of Slovenia
Urban Šilc
Biodiversity & Ecology (2012-09-10) https://doi.org/ghgt9k
DOI: 10.7809/b-e.00215

120. Slovak Vegetation Database
Jozef Šibík
Biodiversity & Ecology (2012-09-10) https://doi.org/ghgt9m
DOI: 10.7809/b-e.00216

121. Ukrainian Grasslands Database
Anna Kuzemko
Biodiversity & Ecology (2012-09-10) https://doi.org/ghk7f3
DOI: 10.7809/b-e.00217

122. The Tree Biodiversity Network (BIOTREE-NET): prospects for biodiversity research and conservation in the Neotropics
Luis Cayuela, Lucía Gálvez-Bravo, Ramón Pérez Pérez, Fábio de Albuquerque, Duncan Golicher, Rakan Zahawi, Neptalí Ramírez-Marcial, Cristina Garibaldi, Richard Field, José Rey Benayas, … Regino Zamora
Biodiversity & Ecology (2012-09-10) https://doi.org/ghgvck
DOI: 10.7809/b-e.00078

123. Timberline meadows along a 1000-km transect in NW North America: species diversity and community patterns
Viktoria Wagner, Toby Spribille, Stefan Abrahamczyk, Erwin Bergmeier
Applied Vegetation Science (2014-01) https://doi.org/f5mpvm
DOI: 10.1111/avsc.12045

124. How resilient are northern hardwood forests to human disturbance? An evaluation using a plant functional group approach
I. Aubin, S. Gachet, C. Messier, A. Bouchard
Ecoscience (2007)

125. Vegetation and altitudinal zonation in continental West Greenland
B. Sieg, B. Drees, F. J. A. Daniëls
Meddelelser om Grønland Bioscience (2006)

126. VegBank – a permanent, open-access archive for vegetation-plot data
Robert Peet, Michael Lee, Michael Jennings, Don Faber-Langendoen
Biodiversity & Ecology (2012-09-10) https://doi.org/ghgvcm
DOI: 10.7809/b-e.00080

127. Vegetation-plot database of the Carolina Vegetation Survey
Robert Peet, Michael Lee, Forbes Boyle, Thomas Wentworth, Michael Schafale, Alan Weakley
Biodiversity & Ecology (2012-09-10) https://doi.org/ghgvcn
DOI: 10.7809/b-e.00081

128. The Alaska Arctic Vegetation Archive (AVA-AK)
Donald A. Walker, Amy L. Breen, Lisa A. Druckenmiller, Lisa W. Wirth, Will Fisher, Martha K. Raynolds, Jozef Šibík, Marilyn D. Walker, Stephan Hennekens, Keith Boggs, … Donatella Zona
Phytocoenologia (2016-09-01) https://doi.org/f877ht
DOI: 10.1127/phyto/2016/0128

129. VegPáramo, a flora and vegetation database for the Andean páramo
Gwendolyn Peyre, Henrik Balslev, David Martí, Petr Sklenář, Paul Ramsay, Pablo Lozano, Nidia Cuello, Rainer Bussmann, Omar Cabrera, Xavier Font
Phytocoenologia (2015-07-01) https://doi.org/f7m9cj
DOI: 10.1127/phyto/2015/0045

130. Insights from a large-scale inventory in the southern Brazilian Atlantic Forest
Alexander Christian Vibrans, André Luís de Gasper, Paolo Moser, Laio Zimermann Oliveira, Débora Vanessa Lingner, Lucia Sevegnani
Scientia Agricola (2020) https://doi.org/ghqcn6
DOI: 10.1590/1678-992x-2018-0036

131. Plant Invasions in Protected Areas
Springer Science and Business Media LLC
(2013) https://doi.org/ghgt8v
DOI: 10.1007/978-94-007-7750-7

Supplementary Material

Table 1: List of databases contributing to sPlotOpen, the environmentally-balanced, open-access, global dataset of vegetation plots. Databases are ordered based on their ID in the Global Index of Vegetation Databases (GVID ID).
GIVD ID Dataset name Custodian Deputy custodian Nr. open-access plots Ref
00-00-001 ForestPlots.net Oliver L. Phillips Aurora Levesley 169 59
00-00-003 SALVIAS Brian Enquist Brad Boyle 3403
00-00-004 Vegetation Database of Eurasian Tundra Risto Virtanen 519
00-00-005 Tundra Vegetation Plots (TundraPlot) Anne D. Bjorkman Sarah Elmendorf 309 60
00-RU-001 Vegetation Database Forest of Southern Ural Vasiliy Martynenko Pavel Shirokikh 68
00-RU-002 Database of Masaryk University`s Vegetation Research in Siberia Milan Chytrý 158 61
00-RU-003 Database Meadows and Steppes of Southern Ural Sergey Yamalov Mariya Lebedeva 238
00-TR-001 Forest Vegetation Database of Turkey - FVDT Ali Kavgacı 45
AF-00-001 West African Vegetation Database Marco Schmidt Georg Zizka 258 62
AF-00-003 BIOTA Southern Africa Biodiversity Observatories Vegetation Database Norbert Jürgens Ute Schmiedel 1015 63
AF-00-006 SWEA-Dataveg Miguel Alvarez Michael Curran 1675
AF-00-008 PANAF Vegetation Database Hjalmar S. Kühl TeneKwetche Sop 884
AF-00-009 Vegetation Database of the Okavango Basin Rasmus Revermann Manfred Finckh 378 64
AF-BF-001 Sahel Vegetation Database Jonas V. Müller Marco Schmidt 556 65
AF-CD-001 Forest Database of Central Congo Basin Kim Sarah Jacobsen Hans Verbeeck 140 66
AF-ET-001 Vegetation Database of Ethiopia Desalegn Wana Anke Jentsch 67 67
AF-MA-001 Vegetation Database of Southern Morocco Manfred Finckh 621 68
AF-ZW-001 Vegetation Database of Zimbabwe Cyrus Samimi 31 69
AS-00-001 Korean Forest Database Tomáš Černý Jiri Dolezal 1039 70
AS-00-003 Vegetation of Middle Asia Arkadiusz Nowak Marcin Nobis 314 71
AS-00-004 Rice Field Vegetation Database Arkadiusz Nowak 32
AS-BD-001 Tropical Forest Dataset of Bangladesh Mohammed A.S. Arfin Khan Fahmida Sultana 87
AS-CN-001 China Forest-Steppe Ecotone Database Hongyan Liu Fengjun Zhao 117 72
AS-CN-002 Tibet-PaDeMoS Grazing Transect Karsten Wesche 58 73
AS-CN-003 Vegetation Database of the BEF China Project Helge Bruelheide 24 74
AS-CN-004 Vegetation Database of the Northern Mountains in China Zhiyao Tang 124
AS-EG-001 Vegetation Database of Sinai in Egypt Mohamed Z. Hatim 143 75
AS-ID-001 Sulawesi Vegetation Database Michael Kessler 24
AS-IR-001 Vegetation Database of Iran Jalil Noroozi Parastoo Mahdavi 277
AS-KZ-001 Database of Meadow Vegetation in the NW Tien Shan Mountains Viktoria Wagner 13 76
AS-MN-001 Southern Gobi Protected Areas Database Henrik von Wehrden Karsten Wesche 1032 77
AS-RU-001 Wetland Vegetation Database of Baikal Siberia (WETBS) Victor Chepinoga 9 78
AS-RU-002 Database of Siberian Vegetation (DSV) Andrey Korolyuk Andrei Zverev 3634 79
AS-RU-004 Database of the University of Münster - Biodiversity and Ecosystem Research Group’s Vegetation Research in Western Siberia and Kazakhstan Norbert Hölzel Wanja Mathar 207
AS-SA-001 Vegetation Database of Saudi Arabia Mohamed Abd El-Rouf Mousa El-Sheikh 711 80
AS-TJ-001 Eastern Pamirs Kim André Vanselow 221 81
AS-TW-001 National Vegetation Database of Taiwan Ching-Feng Li Chang-Fu Hsieh 912
AS-YE-001 Socotra Vegetation Database Michele De Sanctis Fabio Attorre 236 82
AU-AU-002 AEKOS Ben Sparrow 10976 36
AU-NC-001 New Caledonian Plant Inventory and Permanent Plot Network (NC-PIPPN) Jérôme Munzinger Philippe Birnbaum 98 83
AU-NZ-001 New Zealand National Vegetation Databank Susan K. Wiser 1127 84
AU-PG-001 Forest Plots from Papua New Guinea Timothy J.S. Whitfeld George D. Weiblen 60 85
EU-00-002 Nordic-Baltic Grassland Vegetation Database (NBGVD) Jürgen Dengler Łukasz Kozub 54 86
EU-00-011 Vegetation-Plot Database of the University of the Basque Country (BIOVEG) Idoia Biurrun Itziar García-Mijangos 2142 87
EU-00-013 Balkan Dry Grasslands Database Kiril Vassilev Armin Macanović 269 88
EU-00-016 Mediterranean Ammophiletea Database Corrado Marcenò Borja Jiménez-Alfaro 783 89
EU-00-017 European Coastal Vegetation Database John A.M. Janssen 356
EU-00-018 The Nordic Vegetation Database Jonathan Lenoir Jens-Christian Svenning 1735 90
EU-00-019 Balkan Vegetation Database Kiril Vassilev Hristo Pedashenko 484 91
EU-00-020 WetVegEurope Flavia Landucci 127 92
EU-00-022 European Mire Vegetation Database Tomáš Peterka Martin Jiroušek 2560 93
EU-AL-001 Vegetation Database of Albania Michele De Sanctis Giuliano Fanelli 31 94
EU-AT-001 Austrian Vegetation Database Wolfgang Willner Christian Berg 2310 95
EU-BE-002 INBOVEG Els De Bie 119
EU-BG-001 Bulgarian Vegetation Database Iva Apostolova Desislava Sopotlieva 160 96
EU-CH-005 Swiss Forest Vegetation Database Thomas Wohlgemuth 2134 97
EU-CZ-001 Czech National Phytosociological Database Milan Chytrý Ilona Knollová 1287 98
EU-DE-001 VegMV Florian Jansen Christian Berg 15 99
EU-DE-013 VegetWeb Germany Florian Jansen Jörg Ewald 587 100
EU-DE-014 German Vegetation Reference Database (GVRD) Ute Jandt Helge Bruelheide 762 101
EU-DK-002 National Vegetation Database of Denmark Jesper Erenskjold Moeslund Rasmus Ejrnæs 332
EU-ES-001 Iberian and Macaronesian Vegetation Information System (SIVIM) - Wetlands Aaron Pérez-Haase Xavier Font 580
EU-FR-003 SOPHY Emmanuel Garbolino Patrice De Ruffray 7986 102
EU-GB-001 UK National Vegetation Classification Database John S. Rodwell 3182
EU-GR-001 KRITI Erwin Bergmeier 22
EU-GR-005 Hellenic Natura 2000 Vegetation Database (HelNatVeg) Panayotis Dimopoulos Ioannis Tsiripidis 620 103
EU-GR-006 Hellenic Woodland Database Ioannis Tsiripidis Georgios Fotiadis 17 104
EU-HR-001 Phytosociological Database of Non-Forest Vegetation in Croatia Zvjezdana Stančić 193 105
EU-HR-002 Croatian Vegetation Database Željko Škvorc Daniel Krstonošić 585
EU-HU-003 CoenoDat Hungarian Phytosociological Database János Csiky Zoltán Botta-Dukát 46 106
EU-IT-001 VegItaly Roberto Venanzoni Flavia Landucci 754 107
EU-IT-010 Vegetation database of Habitats in the Italian Alps – HabItAlp Laura Casella Pierangela Angelini 247 108
EU-IT-011 Vegetation-Plot Database Sapienza University of Rome (VPD-Sapienza) Emiliano Agrillo Fabio Attorre 967 109
EU-LT-001 Lithuanian Vegetation Database Valerijus Rašomavičius Domas Uogintas 81
EU-LV-001 Semi-natural Grassland Vegetation Database of Latvia Solvita Rūsiņa 369 110
EU-MK-001 Vegetation Database of the Republic of Macedonia Renata Ćušterevska 28
EU-NL-001 Dutch National Vegetation Database Stephan M. Hennekens Joop H.J. Schaminée 1098 111
EU-PL-001 Polish Vegetation Database Zygmunt Kącki Grzegorz Swacha 692 112
EU-RO-007 Romanian Forest Database Adrian Indreica Pavel Dan Turtureanu 166 113
EU-RO-008 Romanian Grassland Database Eszter Ruprecht Kiril Vassilev 82 114
EU-RS-002 Vegetation Database Grassland Vegetation of Serbia Svetlana Aćić Zora Dajić Stevanović 217 115
EU-RU-002 Lower Volga Valley Phytosociological Database Valentin Golub Andrey Chuvashov 383 116
EU-RU-003 Vegetation Database of the Volga and the Ural Rivers Basins Tatiana Lysenko 174 117
EU-RU-011 Vegetation Database of Tatarstan Vadim Prokhorov Maria Kozhevnikova 206 118
EU-SI-001 Vegetation Database of Slovenia Urban Šilc Filip Küzmič 1029 119
EU-SK-001 Slovak Vegetation Database Milan Valachovič Jozef Šibík 2394 120
EU-UA-001 Ukrainian Grasslands Database Anna Kuzemko Yulia Vashenyak 301 121
EU-UA-006 Vegetation Database of Ukraine and Adjacent Parts of Russia Viktor Onyshchenko Vitaliy Kolomiychuk 96
NA-00-002 Tree Biodiversity Network (BIOTREE-NET) Luis Cayuela 241 122
NA-CA-003 Database of Timberline Vegetation in NW North America Viktoria Wagner Toby Spribille 63 123
NA-CA-004 Understory of Sugar Maple Dominated Stands in Quebec and Ontario (Canada) Isabelle Aubin 13 124
NA-CA-005 Boreal Forest of Canada Philippe Marchand Yves Bergeron 57
NA-GL-001 Vegetation Database of Greenland Birgit Jedrzejek Fred J.A. Daniëls 441 125
NA-US-002 VegBank Robert K. Peet Michael T. Lee 14965 126
NA-US-006 Carolina Vegetation Survey Database Robert K. Peet Michael T. Lee 3263 127
NA-US-014 Alaska-Arctic Vegetation Archive Donald A. Walker Amy Breen 771 128
SA-00-002 VegPáramo Gwendolyn Peyre Xavier Font 2010 129
SA-AR-002 Vegetation Database of Central Argentina Melisa Giorgis Alicia T.R. Acosta 86
SA-BO-003 Bolivia Forest Plots Michael Kessler Sebastian Herzog 44
SA-BR-002 Forest Inventory, State of Santa Catarina, Brazil (IFFSC Project) Alexander Christian Vibrans André Luís de Gasper 1561 130
SA-BR-003 Grasslands of Rio Grande do Sul, Brazil Eduardo Vélez-Martin Valério D. Pillar 306
SA-BR-004 Grassland Database of Campos Sulinos Gerhard E. Overbeck Valério D. Pillar 147
SA-CL-002 SSAForests_Plots_db Alvaro G. Gutiérrez 155
SA-CL-003 Chilean Park Transects - Fondecyt 1040528 Aníbal Pauchard Alicia Marticorena 44 131
SA-EC-001 Ecuador Forest Plot Database Jürgen Homeier 166
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. Variable types can be n - nominal (i.e., qualitative variable), o - ordinal, q - quantitative, or b - binary (i.e., boolean), or d - date . Additional details on the variables are in Bruelheide et al. (2019) [23]. GIVD codes derive from Dengler et al. (2011) [37]. Biomes refer to Schultz 2005 [49], modified to include also the world mountain regions by Körner et al. (2017)[50]. The column ESY refers to the EUNIS Habitat Classification Expert system described in Chytrý et al. (2020) [53].
Variable Range/Levels Unit of Measurement Nr. of plots with information Type
GIVD_ID 95104 n
Dataset 95104 n
Continent Africa, Asia, Europe, North America, Oceania, South America 95104 n
Country 95104 n
Biome 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 95104 n
Date_of_recording 1888-07-05 - 2015-02-03 dd-mm-yyyy 80085 d
Latitude -54.82303 - 80.149116 ° (WGS84) 95104 q
Longitude -162.741433 - 176.4221 ° (WGS84) 95104 q
Location_uncertainty 1 - 2750 m 95075 q
Releve_area 0.03 - 40000 m2 67022 q
Plant_recorded All vascular plants, All trees & dominant understory, Dominant trees, Only dominant species, Dominant woody plants >= 2.5 cm dbh, All woody plants, Woody plants >= 1 cm dbh, Woody plants >= 2.5 cm dbh, Woody plants >= 5 cm dbh, Woody plants >= 10 cm dbh, Woody plants >= 20 cm dbh, Woody plants >= 1 m height, Not specified 95104 n
Elevation -30 - 5960 m a.s.l. 62968 q
Aspect 1 - 360 ° 42178 q
Slope 0 - 90 ° 51246 q
is_forest FALSE = 45735; TRUE = 38282 84017 b
ESY 39632 n
Naturalness 1 = Natural, 2 = Semi-natural 60192 o
Forest FALSE = 36282; TRUE = 33170 69452 b
Shrubland FALSE = 58245; TRUE = 11207 69452 b
Grassland FALSE = 33800; TRUE = 35652 69452 b
Wetland FALSE = 59196; TRUE = 10256 69452 b
Sparse_vegetation FALSE = 66177; TRUE = 3275 69452 b
Cover_total 1 - 990 % 19407 q
Cover_tree_layer 0.5 - 150 % 12094 q
Cover_shrub_layer 0.5 - 170 % 16804 q
Cover_herb_layer 0.2 - 199 % 29668 q
Cover_moss_layer 1 - 100 % 9681 q
Cover_lichen_layer 1 - 90 % 708 q
Cover_algae_layer 1 - 100 % 41 q
Cover_litter_layer 1 - 107 % 3161 q
Cover_bare_rocks 1 - 100 % 2747 q
Cover_cryptogams 1 - 90 % 772 q
Cover_bare_soil 0 - 99 % 2746 q
Height_trees_highest 1 - 99 m 8220 q
Height_trees_lowest 1 - 90 m 447 q
Height_shrubs_highest 0.1 - 9.9 m 3389 q
Height_shrubs_lowest 0.1 - 9 m 263 q
Height_herbs_average 0.1 - 600 cm 5901 q
Height_herbs_lowest 1 - 150 cm 490 q
Height_herbs_highest 1 - 600 cm 1083 q
SoilClim_PC1 -6.233 - 8.172 95104 q
SoilClim_PC2 -4.824 - 15.466 95104 q
Resample_1 FALSE = 45317; TRUE = 49787 95104 b
Resample_2 FALSE = 45293; TRUE = 49811 95104 b
Resample_3 FALSE = 45315; TRUE = 49789 95104 b
Resample_1_consensus FALSE = 41842; TRUE = 53262 95104 b

Supplementary Material

Figure S1

Figure S1: Global principal component analysis (PCA) of the world environmental conditions. The PCA is based on the matrix of all terrestrial grid cells (n = 8,384,404, spatial grain = 2.5 arcmin) by 30 environmental variables. The PCA space represents the full environmental space of all terrestrial habitats on Earth, irrespective of whether a grid cell hosted vegetation plots from the sPlotOpen or not. The PCA space is divided into a 10,000 regular tiles (100 x 100), and the number of 2.5 arcmin terrestrial grid cells counted for each tile. Abbreviations - Climate - Bio1 = Annual Mean Temperature, Bio2 = Mean Diurnal Range, Bio3 = Isothermality, Bio4 = Temperature Seasonality, Bio5 = Max Temperature of Warmest Month, Bio6 = Min Temperature of Coldest Month, Bio7 = Temperature Annual Range, Bio8 = Mean Temperature of Wettest Quarter, Bio9 = Mean Temperature of Driest Quarter, Bio10 = Mean Temperature of Warmest Quarter, Bio11 = Mean Temperature of Coldest Quarter, Bio12 = Annual Precipitation, Bio13 = Precipitation of Wettest Month, Bio14 = Precipitation of Driest Month, Bio15 = Precipitation Seasonality, Bio16 = Precipitation of Wettest Quarter, Bio17 = Precipitation of Driest Quarter, Bio18 = Precipitation of Warmest Quarter, Bio19 = Precipitation of Coldest Quarter. Soil - CECSOL = Cation Exchange capacity of soil, ORCDRC = Soil Organic Carbon Content, PHIHOX = Soil pH, BLDFIE = Bulk Density, CLYPPT = Clay mass fraction, SLTPPT = Silt mass fraction, SNDPPT = Sand mass fraction, CRFVOL = Coarse fragments.