Received: 5 October 2018 | Revised: 5 November 2018 | Accepted: 15 November 2018 DOI: 10.1111/jvs.12710 REPORT Journal of Vegetation Science sPlot – A new tool for global vegetation analyses Helge Bruelheide1,2,* | Jürgen Dengler2,3,4,* | Borja Jiménez-Alfaro1,2,5,* | 1,2, 6 7 Oliver Purschke * | Stephan M. Hennekens | Milan Chytrý | Valério 8 9 2,10 11 D. Pillar | Florian Jansen | Jens Kattge | Brody Sandel | Isabelle Aubin12 | Idoia Biurrun13 | Richard Field14 | Sylvia Haider1,2 | Ute Jandt1,2 | Jonathan Lenoir15 | Robert K. Peet16 | Gwendolyn Peyre17 | Francesco Maria Sabatini1,2 | Marco Schmidt18 | Franziska Schrodt14 | Marten Winter2 | Svetlana Aćić19 | Emiliano Agrillo20 | Miguel Alvarez21 | Didem Ambarlı22 | Pierangela Angelini23 | Iva Apostolova24 | Mohammed A. S. Arfin Khan25,26 | 27 20 28,29 Elise Arnst | Fabio Attorre | Christopher Baraloto | Michael Beckmann30 | 31 32 33 34,35 Christian Berg | Yves Bergeron | Erwin Bergmeier | Anne D. Bjorkman | 36 37 38 39 Viktoria Bondareva | Peter Borchardt | Zoltán Botta-Dukát | Brad Boyle | 40 41 42 43 Amy Breen | Henry Brisse | Chaeho Byun | Marcelo R. Cabido | 23 44 45 Laura Casella | Luis Cayuela | Tomáš Černý | Victor Chepinoga46 | 47 48 49 János Csiky | Michael Curran | Renata Ćušterevska | Zora Dajić Stevanović19 | Els De Bie50 | Patrice de Ruffray51 | Michele De Sanctis20 | 52 53 54 Panayotis Dimopoulos | Stefan Dressler | Rasmus Ejrnæs | Mohamed Abd El-Rouf 55,56 39 57 Mousa El-Sheikh | Brian Enquist | Jörg Ewald | Jaime Fagúndez58 | 59 60 61 62 Manfred Finckh | Xavier Font | Estelle Forey | Georgios Fotiadis | 13 63 36 Itziar García-Mijangos | André Luis de Gasper | Valentin Golub | Alvaro 64 65 66 G. Gutierrez | Mohamed Z. Hatim | Tianhua He | Pedro Higuchi67 | 7 68 69 70 Dana Holubová | Norbert Hölzel | Jürgen Homeier | Adrian Indreica | 71 72 6 68 Deniz Işık Gürsoy | Steven Jansen | John Janssen | Birgit Jedrzejek | 7,73 59 74 75 Martin Jiroušek | Norbert Jürgens | Zygmunt Kącki | Ali Kavgacı | 76 77 7 Elizabeth Kearsley | Michael Kessler | Ilona Knollová | Vitaliy Kolomiychuk78 | Andrey Korolyuk79 | Maria Kozhevnikova80 | Łukasz Kozub81 | Daniel Krstonošić82 | Hjalmar Kühl2,83 | Ingolf Kühn1,2,84 | Anna Kuzemko85 | Filip Küzmič86 | *These authors should be considered joint first authors. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2019 The Authors. Journal of Vegetation Science published by John Wiley & Sons Ltd on behalf of International Association of Vegetation Science J Veg Sci. 2019;30:161–186. wileyonlinelibrary.com/journal/jvs | 161 162 | Journal of Vegetation Science BRUELHEIDE et al. Flavia Landucci7 | Michael T. Lee87 | Aurora Levesley88 | Ching-Feng Li89 | 90 88 91,92 Hongyan Liu | Gabriela Lopez-Gonzalez | Tatiana Lysenko | Armin Macanović93 | Parastoo Mahdavi94 | Peter Manning35 | Corrado Marcenò13 | 95 96 Vassiliy Martynenko | Maurizio Mencuccini | Vanessa Minden97 | Jesper 54 98 99 Erenskjold Moeslund | Marco Moretti | Jonas V. Müller | 100 101 102 Jérôme Munzinger | Ülo Niinemets | Marcin Nobis | Jalil Noroozi103 | Arkadiusz Nowak104 | Viktor Onyshchenko85 | Gerhard E. Overbeck8 | Wim 6 105 106 A. Ozinga | Anibal Pauchard | Hristo Pedashenko | Josep Peñuelas107,108 | 109,110 7 111 88 Aaron Pérez-Haase | Tomáš Peterka | Petr Petřík | Oliver L. Phillips | 80 112 59 Vadim Prokhorov | Valerijus Rašomavičius | Rasmus Revermann | 113 114 115 John Rodwell | Eszter Ruprecht | Solvita Rūsiņa | Cyrus Samimi116 | Joop 6 59 117 86 H.J. Schaminée | Ute Schmiedel | Jozef Šibík | Urban Šilc | 82 118 2,83 Željko Škvorc | Anita Smyth | Tenekwetche Sop | Desislava Sopotlieva24 | Ben Sparrow118 | Zvjezdana Stančić119 | Jens-Christian Svenning34 | 74 90 120 Grzegorz Swacha | Zhiyao Tang | Ioannis Tsiripidis | Pavel Dan Turtureanu121 | Emin Uğurlu122 | Domas Uogintas112 | Milan Valachovič117 | Kim André Vanselow123 | Yulia Vashenyak124 | Kiril Vassilev24 | Eduardo Vélez-Martin8 | 125 126 Roberto Venanzoni | Alexander Christian Vibrans | Cyrille Violle127 | Risto Virtanen2,128,129 | Henrik von Wehrden130 | Viktoria Wagner131 | Donald A. Walker132 | Desalegn Wana133 | Evan Weiher134 | Karsten Wesche2,135,136 | Timothy Whitfeld137 | Wolfgang Willner103,138 | Susan Wiser27 | 139 140 53 Thomas Wohlgemuth | Sergey Yamalov | Georg Zizka | Andrei Zverev141 1 Institute of Biology/Geobotany and Botanical Garden, Martin Luther University Halle-Wittenberg, Halle, Germany 2 German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany 3 Vegetation Ecology Group, Institute of Natural Resource Sciences (IUNR), Zurich University of Applied Sciences (ZHAW), Wädenswil, Switzerland 4 Plant Ecology, Bayreuth Center of Ecology and Environmental Research (BayCEER), University of Bayreuth, Bayreuth, Germany 5 Research Unit of Biodiversity (CSUC/UO/PA), University of Oviedo, Mieres, Spain 6 Wageningen Environmental Research (Alterra), Wageningen University and Research, Wageningen, The Netherlands 7 Department of Botany and Zoology, Masaryk University, Brno, Czech Republic 8 Department of Ecology, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil 9 Faculty of Agricultural and Environmental Sciences, University of Rostock, Rostock, Germany 10 Max Planck Institute for Biogeochemistry, Jena, Germany 11 Department of Biology, Santa Clara University, Santa Clara, California 12 Great Lakes Forestry Centre, Canadian Forest Service, Natural Resources Canada, Sault Ste Marie, Ontario, Canada 13 Plant Biology and Ecology, University of the Basque Country UPV/EHU, Bilbao, Spain 14 School of Geography, University of Nottingham, Nottingham, UK 15 Ecologie et Dynamiques des Systèmes Anthropisés (EDYSAN, UMR 7058 CNRS-UPJV), Université de Picardie Jules Verne, Amiens, France 16 Department of Biology, University of North Carolina, Chapel Hill, North Carolina 17 Department of Civil and Environmental Engineering, University of the Andes, Bogota, Colombia 18 Data and Modelling Centre, Senckenberg Biodiversity and Climate Research Centre (BiK-F), Frankfurt am Main, Germany BRUELHEIDE et al. Journal of Vegetation Science | 163 19 Department of Agrobotany, Faculty of Agriculture, Belgrade-Zemun, Serbia 20 Department of Environmental Biology, “Sapienza” University of Rome, Rome, Italy 21 Plant Nutrition, INRES, University of Bonn, Bonn, Germany 22 Department of Agricultural Biotechnology, Faculty of Agriculture and Natural Sciences, Düzce University, Düzce, Turkey 23 Biodiversity Conservation Department, ISPRA – Italian National Institute for Environmental Protection and Research, Rome, Italy 24 Department of Plant and Fungal Diversity and Resources, Institute of Biodiversity and Ecosystem Research, Bulgarian Academy of Sciences, Sofia, Bulgaria 25 Forestry & Environmental Science, Shahjalal University of Science & Technology, Sylhet, Bangladesh 26 Disturbance Ecology, Bayreuth Center of Ecology and Environmental Research (BayCEER), University of Bayreuth, Bayreuth, Germany 27 Manaaki Whenua–Landcare Research, Lincoln, New Zealand 28 International Center for Tropical Botany (ICTB), The Kampong of the National Tropical Botanical Garden, Coconut Grove, Florida 29 Department of Biological Sciences, Florida International University, Miami, Florida 30 Landscape Ecology, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany 31 Botanical Garden, University of Graz, Graz, Austria 32 Forest Research Institute, Université du Québec en Abitibi-Témiscamingue, Rouyn-Noranda, Quebec, Canada 33 Vegetation Ecology and Phytodiversity, University of Göttingen, Göttingen, Germany 34 Department of Bioscience, Center for Biodiversity Dynamics in a Changing World (BIOCHANGE) & Section for Ecoinformatics & Biodiversity, Aarhus University, Aarhus C, Denmark 35 Senckenberg Biodiversity and Climate Research Centre (SBiK-F), Frankfurt am Main, Germany 36 Laboratory of Phytocoenology, Institute of Ecology of the Volga River Basin, Togliatti, Russian Federation 37 Institute of Geography, CEN – Center for Earth System Research and Sustainability, University of Hamburg, Hamburg, Germany 38 Institute of Ecology and Botany, MTA Centre for Ecological Research, Vácrátót, Hungary 39 Ecology and Evolutionary Biology, University of Arizona, Tucson, Arizona 40 International Arctic Research Center, University of Alaska, Fairbanks, Alaska 41 Faculté des Sciences, MEP, Marseille Cedex 20, France 42 School of Civil and Environmental Engineering, Yonsei University, Seoul, South Korea 43 Multidisciplinary Institute for Plant Biology (IMBIV – CONICET), University of Cordoba – CONICET, Cordoba, Argentina 44 Department of Biology, Geology, Physics and Inorganic Chemistry, Universidad Rey Juan Carlos, Móstoles, Spain 45 Department of Forest Ecology, Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Praha 6 – Suchdol, Czech Republic 46 Laboratory of Physical Geography and Biogeography, V.B. Sochava Institute of Geography SB RAS, Irkutsk, Russian Federation 47 Department of Ecology, University of Pécs, Pécs, Hungary 48 Institute of Environmental Engineering, Swiss Federal Institute of Technology (ETH) Zürich, Zürich, Switzerland 49 Institute of Biology, Faculty of Natural Sciences and Mathematics, Skopje, Republic of Macedonia 50 Team Biotope Diversity, Research Institute for Nature and Forest (INBO), Brussels, Belgium 51 Institut de Biologie Moléculaire des Plantes (IBMP), Université de Strasbourg, Strasburg, France 52 Department of Biology, Division of Plant Biology, Laboratory of Botany, University of Patras, Patras, Greece 53 Department of Botany and Molecular Evolution, Senckenberg Research Institute, Frankfurt am Main, Germany 54 Department of Bioscience, Aarhus University, Roende, Denmark 55 Botany and Microbiology Department, College of Science, King Saud University, Riyadh, Saudi Arabia 56 Botany Department, Faculty of Science, Damanhour University, Damanhour, Egypt 57 Hochschule Weihenstephan-Triesdorf, University of Applied Sciences, Freising, Germany 58 Faculty of Science, University of A Coruña, A Coruña, Spain 59 Biodiversity, Ecology and Evolution of Plants, Institute for Plant Science & Microbiology, University of Hamburg, Hamburg, Germany 60 Plant Biodiversity Resource Centre, University of Barcelona, Barcelona, Spain 61 Laboratoire Ecodiv, EA 1293 URA IRSTEA, Normandie University, Mont-Saint-Aignan, France 62 Department of Forestry & Natural Environment Management, TEI of Sterea Ellada, Karpenissi, Greece 63 Department of Natural Science, Regional University of Blumenau, Blumenau, Brazil 64 Departamento de Ciencias Ambientales y Recursos Naturales Renovables, Facultad de Ciencias Agronomicas, Universidad de Chile, Santiago, Chile 65 Botany, Faculty of Science, Tanta University, Tanta, Egypt 66 School of Molecular and Life Sciences, Curtin University, Bentley, Australia 67 Forestry Department, Santa Catarina State University, Lages, Brazil 68 Institute of Landscape Ecology, University of Münster, Münster, Germany 69 Plant Ecology and Ecosystems Research, University of Göttingen, Göttingen, Germany 70 Department of Silviculture, Transilvania University of Brasov, Brasov, Romania 164 | Journal of Vegetation Science BRUELHEIDE et al. 71 Department of Biology, Celal Bayar University, Manisa, Turkey 72 Institute of Systematic Botany and Ecology, Faculty of Natural Sciences, Ulm University, Ulm, Germany 73 Department of Plant Biology, Mendel University in Brno, Brno, Czech Republic 74 Botanical Garden, University of Wrocław, Wrocław, Poland 75 Silviculture and Forest Botany, Southwest Anatolia Forest Research Institute, Antalya, Turkey 76 Department of Environment, Ghent University, Gent, Belgium 77 Department of Systematic and Evolutionary Botany, University of Zurich, Zurich, Switzerland 78 O.V. Fomin Botanical Garden at the Educational and Scientific Centre, Institute of Biology and Medicine, Taras Shevchenko National University of Kyiv, Kyiv, Ukraine 79 Geosystem Laboratory, Central Siberian Botanical Garden, Siberian Branch, Russian Academy of Sciences, Novosibirsk, Russian Federation 80 Institute of Environmental Sciences, Kazan Federal University, Kazan, Russian Federation 81 Department of Plant Ecology and Environmental Conservation, Faculty of Biology, Biological and Chemical Research Centre, University of Warsaw, Warsaw, Poland 82 Faculty of Forestry, University of Zagreb, Zagreb, Croatia 83 Primatology, Max Planck Institute for Evolutionary Anthropology (MPI-EVA), Leipzig, Germany 84 Department of Community Ecology, Helmholtz Centre for Environmental Research – UFZ, Halle, Germany 85 M.G. Kholodny Institute of Botany, National Academy of Sciences of Ukraine, Kyiv, Ukraine 86 Institute of Biology, Research Centre of Slovenian Academy of Sciences and Arts (ZRC SAZU), Ljubljana, Slovenia 87 NatureServe, Durham, North Carolina 88 School of Geography, University of Leeds, Leeds, UK 89 School of Forestry and Resource Conservation, National Taiwan University, Hsinchu, Taiwan 90 College of Urban and Environmental Sciences, Peking University, Beijing, China 91 Department of the Phytodiversity Problems, Institute of Ecology of the Volga River Basin RAS, Togliatti, Russian Federation 92 Laboratory of Vegetation Science, Komarov Botanical Institute RAS, Saint-Petersburg, Russia 93 Department of Biology, Center for Ecology and Natural Resources – Academician Sulejman Redžić, University of Sarajevo, Sarajevo, Bosnia and Herzegovina 94 Research Group Vegetation Science & Nature Conservation, Department of Ecology and Environmental Science, Carl von Ossietzky-University Oldenburg, Oldenburg, Germany 95 Ufa Institute of Biology of Ufa Federal Scientific Centre of the Russian Academy of Sciences, Ufa, Russian Federation 96 Centre Research Ecology and Forestry Applications (CREAF), ICREA, Barcelona, Spain 97 Institute of Biology an Environmental Sciences, Carl von Ossietzky-University Oldenburg, Oldenburg, Germany 98 Biodiversity and Conservation Biology, Swiss Federal Research Institute WSL, Birmensdorf, Switzerland 99 Conservation Science, Royal Botanic Gardens, Kew, UK 100 AMAP – Botany and Modelling of Plant Architecture and Vegetation, IRD, CIRAD, CNRS, INRA, Université Montpellier, Montpellier, France 101 Crop Science and Plant Biology, Estonian University of Life Sciences, Tartu, Estonia 102 Institute of Botany, Jagiellonian University, Kraków, Poland 103 Department of Botany and Biodiversity Research, University of Vienna, Vienna, Austria 104 Botanical Garden – Center for Biological Diversity Conservation, Polish Academy of Sciences, Warszawa, Poland 105 Laboratorio de Invasiones Biológicas (LIB), University of Concepción, Concepción, Chile 106 Amsterdam, The Netherlands 107 Global Ecology Unit CREAF-CSIC-UAB, CSIC, Bellaterra, Spain 108 CREAF, Cerdanyola del Vallès, Spain 109 Department of Evolutionary Biology, Ecology and Environmental Sciences, University of Barcelona, Barcelona, Spain 110 Continental Ecology, Center for Advanced Studies of Blanes, Spanish Research Council (CEAB-CSIC), Blanes, Girona, Spain 111 Department of GIS and Remote Sensing, Institute of Botany, The Czech Academy of Sciences, Průhonice, Czech Republic 112 Institute of Botany, Nature Research Centre, Vilnius, Lithuania 113 Lancaster, UK 114 Hungarian Department of Biology and Ecology, Faculty of Biology and Geology, Babeș-Bolyai University, Cluj-Napoca, Romania 115 Department of Geography, University of Latvia, Riga, Latvia 116 Climatology, Bayreuth Center of Ecology and Environmental Research (BayCEER), University of Bayreuth, Bayreuth, Germany 117 Institute of Botany, Plant Science and Biodiversity Centre, Slovak Academy of Sciences, Bratislava, Slovakia 118 TERN, University of Adelaide, Adelaide, Australia 119 Faculty of Geotechnical Engineering, University of Zagreb, Varaždin, Croatia 120 School of Biology, Aristotle University of Thessaloniki, Thessaloniki, Greece BRUELHEIDE et al. Journal of Vegetation Science | 165 121 A. Borza Botanical Garden, Babeș-Bolyai University, Cluj-Napoca, Romania 122 Forest Engineering Department, Faculty of Forestry, Bursa Technical University, Yıldırım, Bursa, Turkey 123 Department of Geography, University of Erlangen-Nuremberg, Erlangen, Germany 124 Khmelnytskyi Institute of Interregional Academy of Personnel Management, Khmelnytskyi, Ukraine 125 Department of Chemistry, Biology and Biotechnology, University of Perugia, Perugia, Italy 126 Departamento de Engenharia Florestal, Universidade Regional de Blumenau, Blumenau, Brazil 127 Centre d'Ecologie Fonctionnelle et Evolutive (UMR5175), CNRS – Université de Montpellier – Université Paul-Valéry Montpellier – EPHE, Montpellier, France 128 Ecology and Genetics Research Unit, Biodiversity Unit, University of Oulu, Oulu, Finland 129 Department of Physiological Diversity, Helmholtz Center for Environmental Research – UFZ, Leipzig, Germany 130 Institute of Ecology, Leuphana University, Lüneburg, Germany 131 Department of Biological Sciences, University of Alberta, Edmonton, Canada 132 Institute of Arctic Biology, University of Alaska, Fairbanks, Alaska 133 Department of Geography & Environmental Studies, Addis Ababa University, Addis Ababa, Ethiopia 134 Department of Biology, University of Wisconsin – Eau Claire, Eau Claire, Wisconsin 135 Botany Department, Senckenberg Museum of Natural History Görlitz, Görlitz, Germany 136 International Institute Zittau, Technical University Dresden, Zittau, Germany 137 Department of Ecology and Evolutionary Biology/Brown University Herbarium, Brown University, Providence, Rhode Island 138 Vienna Institute for Nature Conservation & Analyses, Vienna, Austria 139 Research Unit Forest Dynamics, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland 140 Laboratory of Wild-Growing Flora, Botanical Garden-Institute, Ufa Scientific Centre, Russian Academy of Sciences, Ufa, Russian Federation 141 Department of Botany, Tomsk State University, Tomsk, Russian Federation Correspondence Helge Bruelheide, Institute of Biology/ Abstract Geobotany and Botanical Garden, Martin Aims: Vegetation-­plot records provide information on the presence and cover or Luther University Halle-Wittenberg, Halle, Germany. abundance of plants co-­occurring in the same community. Vegetation-­plot data are Email: helge.bruelheide@botanik.uni-halle.de spread across research groups, environmental agencies and biodiversity research Co-ordinating Editor: Alessandro Chiarucci centers and, thus, are rarely accessible at continental or global scales. Here we pre- sent the sPlot database, which collates vegetation plots worldwide to allow for the exploration of global patterns in taxonomic, functional and phylogenetic diversity at the plant community level. Results: sPlot version 2.1 contains records from 1,121,244 vegetation plots, which comprise 23,586,216 records of plant species and their relative cover or abundance in plots collected worldwide between 1885 and 2015. We complemented the infor- mation for each plot by retrieving climate and soil conditions and the biogeographic context (e.g., biomes) from external sources, and by calculating community-­weighted means and variances of traits using gap-­filled data from the global plant trait data- base TRY. Moreover, we created a phylogenetic tree for 50,167 out of the 54,519 species identified in the plots. We present the first maps of global patterns of com- munity richness and community-­weighted means of key traits. Conclusions: The availability of vegetation plot data in sPlot offers new avenues for vegetation analysis at the global scale. KEYWORDS biodiversity, community ecology, ecoinformatics, functional diversity, global scale, macroecology, phylogenetic diversity, plot database, sPlot, taxonomic diversity, vascular plant, vegetation relevé 166 | Journal of Vegetation Science BRUELHEIDE et al. 1 | I NTRO D U C TI O N datasets have been derived from occurrence data (i.e., presence-­ only data), and after being aggregated spatially, have a relatively Studying global biodiversity patterns is at the core of macroecologi- coarse-­grain scale (e.g., one-­degree grid cells) without information cal research (Costello, Wilson, & Houlding, 2012; Kreft & Jetz, 2007; on species co-­occurrence at the meaningful scale of local communi- Wiens, 2011), since their exploration may provide insights into the ties (Boakes et al., 2010). In contrast, vegetation-­plot data record the ecological and evolutionary processes acting at different spatio-­ cover or abundance of each plant species that occurs in a plot of a temporal scales (Ricklefs, 2004). The opportunities engendered by given size at the date of the survey, representing the main reservoir the compilation of large collections of biodiversity data into widely of plant community data worldwide (Dengler et al., 2011). accessible global (GBIF, www.gbif.org) or continental databases (e.g., Vegetation-­plot data differ in fundamental ways from databases BIEN, www.bien.nceas.ucsb.edu/bien) have recently advanced our of occurrence records of individual species aggregated at the level understanding of global biodiversity patterns, especially for verte- of grid cells or regions of hundreds or thousands of square kilome- brates, but also for vascular plants (Butler et al., 2017; Engemann ters (Figure 1). First, vegetation plots usually provide information on et al., 2016; Lamanna et al., 2014; Swenson et al., 2012). Although the relative cover or relative abundance of species, allowing for the this development has led to the formulation of several macroecolog- testing of central theories of biogeography, such as the abundance– ical theories (Currie et al., 2004; Pärtel, Bennett, & Zobel, 2016), a range size relationship (Gaston & Curnutt, 1998) or the relationship more mechanistic understanding of how assembly processes shape between local abundance and niche breadth (Gaston et al., 2000). ecological communities, and consequently global biodiversity pat- Second, they contain information on which plant species co-­occur in terns, is still missing (Lessard, Belmaker, Myers, Chase, & Rahbek, the same locality (Chytrý et al., 2016), which is a necessary precon- 2012). dition for direct biotic interactions among plant individuals. Third, Understanding the links between biodiversity patterns and as- unrecorded species can be considered truly absent from the abo- sembly processes requires fine-­grain data on the co-­occurrence of veground vegetation at this scale because the standardized meth- species in ecological communities, sampled across continental or odology of taking a vegetation record requires a systematic search global spatial extents (Beck et al., 2012; Wisz et al., 2013). For ex- for all species in a plot, or at least all species of the dominant func- ample, such co-­occurrence data have been used to compare changes tional group. Fourth, many plots are spatially explicit and can be in vegetation composition over time spans of decades (Jandt, von resurveyed through time to assess possible consequences of land Wehrden, & Bruelheide, 2011; Perring et al., 2018). Unfortunately, use and climate change (Perring et al., 2018; Steinbauer et al., 2018). up to now information on fine-­grain vegetation data has not been Fifth, vegetation plots represent a snapshot of the primary produc- readily available, as most of the continental to global biodiversity ers of a terrestrial ecosystem, which can be functionally linked to F I G U R E 1 Conceptual figure visualizing how functional composition (in this case plant height) differs between calculations based on mean traits for grid cells and community data sampled in vegetation plots. Occurrence data (e.g., from distribution atlases, GBIF, etc.) can be used to calculate mean trait values in grid cells G1–G3. However, community weighted means (CWMs) of traits differ across local plots (P1–P6), while the mean values of CWMs in the grid cells differ from the unweighted values calculated in the grid cells. This example is simplified by showing few species and few plots. In reality, differences are generally more pronounced BRUELHEIDE et al. Journal of Vegetation Science | 167 organisms from different trophic groups sampled in the same plots in global-­scale biodiversity research (Franklin, Serra-­Diaz, Syphard, & (e.g., multiple-­t axa surveys) and related processes and services both Regan, 2017; Wiser, 2016). This is unfortunate because vegetation-­ below (e.g., decomposition, nutrient cycling) and above ground (e.g., plot data may reveal important patterns that cannot be captured by herbivory, pollination) (e.g., Schuldt et al., 2018). grid-­based datasets (Table 1). Functional composition patterns, for Recently several projects at the regional to continental scale instance, may differ substantially when considering vegetation-­plot have demonstrated the potential of using vegetation-­plot databases data rather than single species occurrences aggregated at the level of for exploring biodiversity patterns and the underlying assembly pro- coarse-­grain grid cells. Using plant height as an illustration reveals that cesses. Using vegetation data of French grasslands, Borgy et al. (2017) the trait means calculated on all the species occurring in a grid cell may demonstrated that weighting leaf traits by species abundance in local differ strongly from the community-­weighted means (CWMs) aver- communities is pivotal to capture leaf trait–environment relationships. aged across local communities (Figure 1). Nevertheless, only the grid-­ Analyzing United States forest assemblages surveyed at the commu- based approach has been used to date in studies of the geographic nity level, Šímová, Rueda, and Hawkins (2017) were able to relate cold distribution of trait values (e.g., Swenson et al., 2012, 2017; Wright or drought tolerance to leaf traits, dispersal traits and traits related to et al., 2017). stem hydraulics. Using plot-­based tree inventories of the United States Here, we present sPlot, a global database for compiling and in- forest service, Zhang, Niinemets, Sheffield, and Lichstein (2018) found tegrating plant community data. We describe (a) main steps in inte- that shifts in tree functional composition amplify the response of for- grating vegetation-­plot data in a repository that provides taxonomic, est biomass to droughts. Based on >15.000 plots from a wide num- functional and phylogenetic information on co-­occurring plant spe- ber of habitat types in Denmark, Moeslund et al. (2017) showed that cies and links it to global environmental drivers; (b) principal sources typical plant species that are part of the site-­specific species pool but and properties of the data and the procedure for data usage; and (c) are absent in a community tend to depend on mycorrhiza, are mostly expected impacts of the database in future ecological research. To il- adapted to low light and low nutrient levels, have poor dispersal abili- lustrate the potential of sPlot we also show global diversity patterns ties and are ruderals and stress-­intolerant. By collating >40,000 vege- that can be readily derived from the current content. tation plots sampled in European beech forests, Jiménez-­Alfaro et al. (2018) found that current local community diversity and species pool 2 | CO M PI L ATI O N O F TH E S PLOT sizes calculated at different scales were mainly explained by proximity DATA BA S E to glacial refugia and current precipitation. Although large collections of vegetation-­plot data are now 2.1 | Vegetation-­plot data available from national to continental levels (e.g., Chytrý et al., 2016; Enquist, Condit, Peet, Schildhauer, & Thiers, 2016; Peet, Lee, The sPlot consortium currently collates 110 vegetation-­plot data- Jennings, & Faber-­Langendoen, 2012; Schaminée, Hennekens, bases of regional, national or continental extent. Some of the data- Chytrý, & Rodwell, 2009; Schmidt et al., 2012), they are rarely used bases have previously been aggregated by and contributed through TA B L E 1 Types of information provided by single vegetation plots, vegetation plots aggregated within grid cells (or other geographic units) and single species occurrence records aggregated within grid cells. The three levels are illustrated in Figure 1 Information from… Single vegetation plots Set of vegetation plots aggregated within Grid-­cell data from floristic grid cells inventories To derive information Plot level Grid cell level Grid cell level on the … Type of occurrence Co-­occurrence, occurrence by Occurrence by vegetation type Occurrence vegetation type Community assembly Yes (co-­occurrence is a prerequisite No No rules for species interactions) Absences Yes (for the target plant group in a No (except for intensive sampling schemes) Depending on sampling intensity study) Floristic composition … of the local community … of the species pools of vegetation types … of the total set of species Diversity α , γ γ Species abundance Local cover-­abundance Mean cover-­abundance and frequency by Occurrence only vegetation type Combination with Functional composition of the local Functional composition of the species pool Functional composition of the total traits community (traits unweighted or (unweighted or weighted) set of species (unweighted only) weighted by cover: CWM, CWV) Environmental … at the local level … at the regional level … at the regional level filtering 168 | Journal of Vegetation Science BRUELHEIDE et al. two (sub-­)continental database initiatives (Table 2 and Appendix We accepted all names that were matched, or converted from S1). All data from Europe and nearby regions were contributed via synonyms, with an overall match score of 1. In cases with no exact the European Vegetation Archive (EVA), using the SynBioSys taxon match (i.e., the overall match score was <1), names were inspected database as a standard taxonomic backbone (Chytrý et al., 2016). on an individual basis. All names that matched at taxonomic ranks Three African databases were contributed via the Tropical African at or lower than species (e.g., subspecies, varieties) were accepted Vegetation Archive (TAVA). In addition, multiple U.S. databases were as correct names. The name matching procedure was repeated for contributed through the VegBank archive maintained in support of the uncertain names (i.e., with match accuracy scores below the the U.S. National Vegetation Classification (Peet, Lee, Boyle, et al., threshold value from the first matching run), with a preference on 2012; Peet, Lee, Jennings, & Faber-­L angendoen, 2012). The data first using the source ‘Tropicos’ (Missouri Botanical Garden; http:// from other regions (South America, Asia) were contributed as sepa- www.tropicos.org/; accessed 19 Dec 2014) because here matching rate databases. scores were often higher for names of low taxonomic rank. The re- We stored the vegetation-­plot data from the individual databases maining 9,641 non-­matched names were resolved using (a) the addi- in the database software TURBOVEG v2 (Hennekens & Schaminée, tional source ‘NCBI’ (Federhen, 2010) within TNRS, (b) the matching 2001). Our general procedure was to preserve the original structure tools in the Plant List web application (The Plant List 2013), (c) the and content of the databases as much as possible in order to facil- ‘tpl’-­function within the R-­package ‘Taxonstand’ (Cayuela, Stein, & itate regular updates through automated workflows. The individual Oksanen, 2017) and (d) manual inspection (i.e., to resolve vernacular databases were then integrated into a single SQLite database using names). All subspecies were aggregated to the species level. Names TURBOVEG v3 (S.M. Hennekens, ALTERRA, The Netherlands; www. that could not be matched were classified as ‘No suitable matches synbiosys.alterra.nl/turboveg3/help/en/index.html). TURBOVEG v3 found’. Because sPlot and TRY contain taxa of non-­vascular plants, combines the species lists from the original databases in a single re- we tagged vascular plant names based on their family and phylum pository and links the plot attributes (so-­called header data) to 58 affiliation, using the ‘rgbif’ library in R (Chamberlain, 2017). Of the descriptors of vegetation-­plots (Table S2.1 in Appendix S2). The full list of plant names in sPlot and TRY, 79,171 (94.6%) plant names metadata of the databases collated in sPlot were managed through were matched at the species level, 4,343 (5.2%) at the genus level, the Global Index of Vegetation-­Plot Databases (GIVD; Dengler et al., 152 (0.2%) at the family level and 13 names at higher taxonomic lev- 2011), using the GIVD ID as the identifier. The current sPlot version els. Overall, this led to 58,066 accepted taxon names in sPlot. Family 2.1 was created in October 2016 and contains 1,121,244 vegetation affiliation was classified according to APG III (APG III, 2009). A de- plots with 23,586,216 plant species × plot observations (i.e., records tailed description of the workflow, including R-­code, is available in of a species in a plot). Most records (1,073,737; 95.8%) have infor- Purschke (2017a). mation on cover, 29,288 on presence/absence, 5,854 on basal area, One potential shortcoming of our taxonomic backbone is that 4,883 on number of stems (often in addition to basal area), 148 on for most regions it was necessary to standardize taxa using standard importance value (a combination of basal area and number of stems), sets of taxonomic synonyms. Thus, if a taxonomic name represents 3,265 on counts of individuals, 1,895 on percentage frequency, and multiple taxonomic concepts, e.g., such as created by the splitting further 2,174 have a mix of these different types of metrics. and lumping of taxa, or a name has been misapplied in a region, we must trust that this problem has been addressed in our component databases (Franz, Peet, & Weakley, 2004; Jansen & Dengler, 2010). 2.2 | Taxonomic standardization However, different component databases may have applied differ- To combine the species lists of the different databases in sPlot, we ent taxonomic concepts for splitting and lumping taxa. constructed a taxonomic backbone. To link co-­occurrence informa- tion in sPlot with plant traits, we expanded this backbone to inte- 2.3 | Physiognomic information grate plant names used in the TRY database (Kattge et al., 2011). The taxon names (without nomenclatural authors) from sPlot 2.1 and To achieve a classification into forests versus non-­forests that is ap- TRY 3.0 were first concatenated into one list, resulting in 121,861 plicable to all plots irrespective of the structural and habitat data names, of which 61,588 (50.5%) were unique to sPlot; 35,429 provided by the source database, we defined as forest all plot re- (29.1%) unique to TRY; and 24,844 (20.4%) shared between TRY and cords that had >25% absolute cover of the tree layer, making use of sPlot. Taxon names were parsed and resolved using the Taxonomic the attribute data of sPlot. This threshold is similar to the classifica- Name Resolution Service web application (TNRS version 4.0; Boyle tion of Ellenberg and Müller-­Dombois (1967), who defined woodland et al., 2013; iPlant Collaborative, 2015), using the five TNRS stand- formations with trees covering more than 30%. There were 16,244 ard sources ranked by default. We allowed for (a) partial matching tree species in the sPlot database. As tree layer cover was availa- to the next higher rank (genus or family) if the full taxon name could ble for only 25% of all plots, we additionally used the information not be found and (b) full fuzzy matching, to return names that were whether the taxa present in a plot were trees (usually defined as matched within a maximum number of four single-­character edits being taller than 5 m), using the plant growth form information from (Levenshtein edit distance of 4), which corresponds to the minimum TRY (see below). Thus, plots lacking tree cover information were match accuracy of 0.05 in TNRS, with 1 indicating a perfect match. defined as forests if the sum of relative cover of all tree taxa was BRUELHEIDE et al. Journal of Vegetation Science | 169 TA B L E 2 Plot datasets included in sPlot 2.1 # of plots in GIVD ID Database name sPlot 2.1 Custodian Deputy custodian Reference [Aggregator] European Vegetation Archive 950,001 Milan Chytrý Ilona Knollová Chytrý et al. (2016) (EVA) 00-­0 0-­0 04 Vegetation Database of Eurasian 1,132 Risto Virtanen Tundra 00-­RU-­0 01 Vegetation Database Forest of 1,102 Vassiliy Martynenko Southern Ural 00-­RU-­0 03 Database Meadows and Steppes 2,354 Sergey Yamalov Mariya Lebedeva of Southern Ural 00-­TR-­0 01 Forest Vegetation Database of 919 Ali Kavgacı Turkey -­ FVDT 00-­TR-­0 02* Non-­forest Vegetation Database 3,018 Deniz Işık Gürsoy Didem Ambarlı of Turkey AS-­TR-­0 02 Vegetation Database of Oak 1,181 Emin Uğurlu Communities in Turkey EU-­0 0-­0 02 Nordic-­Baltic Grassland 7,675 Jürgen Dengler Łukasz Kozub Dengler and Rūsiņa Vegetation Database (NBGVD) (2012) EU-­0 0-­011 Vegetation-­Plot Database of the 18,441 Idoia Biurrun Itziar García-­Mijangos Biurrun, García-­ University of the Basque Mijangos, Campos, Country (BIOVEG) Herrera, and Loidi (2012) EU-­0 0-­013 Balkan Dry Grasslands Database 7,683 Kiril Vassilev Armin Macanović Vassilev, Dajič, Ćušterevska, Bergmeier, and Apostolova (2012) EU-­0 0-­016 Mediterranean Ammophiletea 7,359 Corrado Marcenò Borja Jiménez-­Alfaro Marcenò and Database Jiménez-­Alfaro (2017) EU-­0 0-­017 European Coastal Vegetation 4,624 John Janssen Database EU-­0 0-­018 The Nordic Vegetation Database 5,477 Jonathan Lenoir Jens-­Christian Lenoir et al. (2013) Svenning EU-­0 0-­019 Balkan Vegetation Database 9,118 Kiril Vassilev Hristo Pedashenko Vassilev et al. (2016) EU-­0 0-­020 WetVegEurope 14,111 Flavia Landucci Landucci et al. (2015) EU-­0 0-­022 European Mire Vegetation 10,147 Tomáš Peterka Martin Jiroušek Peterka, Jiroušek, Database Hájek, and Jiménez-­Alfaro (2015) EU-­AL-­0 01 Vegetation Database of Albania 290 Michele De Sanctis Giuliano Fanelli De Sanctis, Fanelli, Mullaj, and Attorre (2017) EU-­AT-­0 01 Austrian Vegetation Database 34,458 Wolfgang Willner Christian Berg Willner, Berg, and Heiselmayer (2012) EU-­BE-­0 02 INBOVEG 25,665 Els De Bie EU-­BG-­0 01 Bulgarian Vegetation Database 5,254 Iva Apostolova Desislava Sopotlieva Apostolova, Sopotlieva, Pedashenko, Velev, and Vasilev (2012) EU-­CH-­0 05 Swiss Forest Vegetation 14,193 Thomas Wohlgemuth Wohlgemuth (2012) Database EU-­C Z-­0 01 Czech National Phytosociological 104,697 Milan Chytrý Dana Holubová Chytrý and Rafajová Database (2003) (Continues) 170 | Journal of Vegetation Science BRUELHEIDE et al. TA B L E 2 (Continued) # of plots in GIVD ID Database name sPlot 2.1 Custodian Deputy custodian Reference EU-­DE-­0 01 VegMV 53,822 Florian Jansen Christian Berg Jansen, Dengler, and Berg (2012) EU-­DE-­013 VegetWeb Germany 23,078 Jörg Ewald Ewald, May, and Kleikamp (2012) EU-­DE-­014 German Vegetation Reference 30,840 Ute Jandt Helge Bruelheide Jandt and Bruelheide Database (GVRD) (2012) EU-­DK-­0 02 National Vegetation Database of 24,264 Jesper Erenskjold Rasmus Ejrnæs Denmark Moeslund EU-­ES-­0 01 Iberian and Macaronesian 6,560 Aaron Pérez-­Haase Xavier Font Vegetation Information System (SIVIM) ̶ Wetlands EU-­FR-­0 03 SOPHY 209,864 Henry Brisse Patrice de Ruffray Brisse, de Ruffray, Grandjouan, and Hoff (1995) EU-­GB-­0 01 UK National Vegetation 28,533 John S. Rodwell Classification Database EU-­GR-­0 01 KRITI 292 Erwin Bergmeier EU-­GR-­0 05 Hellenic Natura 2000 Vegetation 5,168 Panayotis Dimopoulos Ioannis Tsiripidis Dimopoulos and Database (HelNatVeg) Tsiripidis (2012) EU-­GR-­0 06 Hellenic Woodland Database 3,199 Georgios Fotiadis Ioannis Tsiripidis Fotiadis, Tsiripidis, Bergmeier, and Dimopoulos (2012) EU-­HR-­0 01 Phytosociological Database of 5,057 Zvjezdana Stančić Stančić (2012) Non-­Forest Vegetation in Croatia EU-­HR-­0 02 Croatian Vegetation Database 8,734 Željko Škvorc Daniel Krstonošić EU-­HU-­0 03 CoenoDat Hungarian 8,505 János Csiky Zoltán Botta-­Dukát Lájer et al. (2008) Phytosociological Database EU-­IT-­0 01 VegItaly 15,332 Roberto Venanzoni Flavia Landucci Landucci et al. (2012) EU-­IT-­010 Italian National Vegetation 3,562 Laura Casella Pierangela Angelini Casella, Bianco, Database (BVN/ISPRA) Angelini, and Morroni (2012) EU-­IT-­011 Vegetation-­Plot Database 12,780 Emiliano Agrillo Fabio Attorre Agrillo et al. (2017) Sapienza University of Rome (VPD-­Sapienza) EU-­LT-­0 01 Lithuanian Vegetation Database 7,821 Valerijus Rašomavičius Domas Uogintas EU-­LV-­0 01 Semi-­natural Grassland 5,594 Solvita Rūsiņa Rūsiņa (2012) Vegetation Database of Latvia EU-­MK-­0 01 Vegetation Database of the 1,417 Renata Ćušterevska Republic of Macedonia EU-­NL-­0 01 Dutch National Vegetation 102,327 Joop H.J. Schaminée Stephan M. Schaminée et al. Database Hennekens (2006) EU-­PL-­0 01 Polish Vegetation Database 22,229 Zygmunt Kącki Grzegorz Swacha Kącki and Śliwiński (2012) EU-­RO-­0 07 Romanian Forest Database 6,017 Adrian Indreica Pavel Dan Turtureanu Indreica, Turtureanu, Szabó, and Irimia (2017) EU-­RO-­0 08 Romanian Grassland Database 1,921 Eszter Ruprecht Kiril Vassilev Vassilev et al. (2018) EU-­RS-­0 02 Vegetation Database Grassland 5,587 Svetlana Aćić Zora Dajić Stevanović Aćić, Petrović, Šilc, Vegetation of Serbia and Dajić Stevanović (2012) (Continues) BRUELHEIDE et al. Journal of Vegetation Science | 171 TA B L E 2 (Continued) # of plots in GIVD ID Database name sPlot 2.1 Custodian Deputy custodian Reference EU-­RU-­0 02 Lower Volga Valley 14,853 Valentin Golub Viktoria Bondareva Golub et al. (2012) Phytosociological Database EU-­RU-­0 03 Vegetation Database of the 1,516 Tatiana Lysenko Lysenko, Volga and the Ural Rivers Mitroshenkova, and Basins Kalmykova (2012) EU-­RU-­011 Vegetation Database of 7,471 Vadim Prokhorov Maria Kozhevnikova Prokhorov, Rogova, Tatarstan and Kozhevnikova (2017) EU-­SI-­0 01 Vegetation Database of Slovenia 10,986 Urban Šilc Filip Küzmič Šilc (2012) EU-­SK-­0 01 Slovak Vegetation Database 36,405 Milan Valachovič Jozef Šibík Šibík (2012) EU-­UA-­0 01 Ukrainian Grasslands Database 4,043 Anna Kuzemko Yulia Vashenyak Kuzemko (2012) EU-­UA-­0 06 Vegetation Database of Ukraine 3,326 Viktor Onyshchenko Vitaliy Kolomiychuk and Adjacent Parts of Russia [Aggregator] Tropical African Vegetation 6,677 Marco Schmidt Stefan Dressler Janßen et al. (2011) Archive (TAVA) AF-­0 0-­0 01 West African Vegetation 3,129 Marco Schmidt Georg Zizka Schmidt et al. (2012) Database AF-­0 0-­0 08 PANAF Vegetation Database 2,469 Hjalmar Kühl TeneKwetche Sop AF-­BF-­0 01 Sahel Vegetation Database 1,079 Jonas V. Müller Marco Schmidt Müller (2003) Other databases 164,566 00-­0 0-­0 01 RAINFOR data managed by 1,827 Oliver L. Phillips Aurora Levesley Lopez-­Gonzalez, ForestPlots.net Lewis, Burkitt, and Phillips (2011) 00-­0 0-­0 03 SALVIAS 4,883 Brian Enquist Brad Boyle 00-­0 0-­0 05 Tundra Vegetation Plots 577 Anne D. Bjorkman Sarah Elmendorf Elmendorf et al. (TundraPlot) (2012) 00-­RU-­0 02 Database of Masaryk 1,547 Milan Chytrý Chytrý (2012) University’s Vegetation Research in Siberia AF-­0 0-­0 03 BIOTA Southern Africa 1,666 Norbert Jürgens Gerhard Muche Muche, Schmiedel, Biodiversity Observatories and Jürgens (2012) Vegetation Database AF-­0 0-­0 06 SWEA-­Dataveg 2,704 Miguel Alvarez Michael Curran AF-­0 0-­0 09 Vegetation Database of the 590 Rasmus Revermann Manfred Finckh Revermann et al. Okavango Basin (2016) AF-­CD-­0 01 Forest Database of Central 292 Elizabeth Kearsley Hans Verbeeck Kearsley et al. (2013) Congo Basin AF-­E T-­0 01 Vegetation Database of Ethiopia 74 Desalegn Wana Anke Jentsch Wana and Beierkuhnlein (2011) AF-­MA-­0 01 Vegetation Database of 1,337 Manfred Finckh Finckh (2012) Southern Morocco AF-­Z A-­0 03* SynBioSys Fynbos Vegetation 3,810 John Janssen Database AF-­Z W-­0 01* Vegetation Database of 36 Cyrus Samimi Samimi (2003) Zimbabwe AS-­0 0-­0 01 Korean Forest Database 4,885 Tomáš Černý Petr Petřík Černý et al. (2015) AS-­0 0-­0 03 Vegetation of Middle Asia 1,381 Arkadiusz Nowak Marcin Nobis Nowak et al. (2017) AS-­0 0-­0 04 Rice Field Vegetation Database 179 Arkadiusz Nowak (Continues) 172 | Journal of Vegetation Science BRUELHEIDE et al. TA B L E 2 (Continued) # of plots in GIVD ID Database name sPlot 2.1 Custodian Deputy custodian Reference AS-­BD-­0 01 Tropical Forest Dataset of 211 Mohammed A.S. Arfin Fahmida Sultana Bangladesh Khan AS-­CN-­0 01 China Forest-­Steppe Ecotone 148 Hongyan Liu Fengjun Zhao Liu, Cui, Pott, and Database Speier (2000) AS-­CN-­0 02 Tibet-­PaDeMoS Grazing 146 Karsten Wesche Wang et al. (2017) Transect AS-­CN-­0 03* Vegetation Database of the BEF 27 Helge Bruelheide Bruelheide et al. China Project (2011) AS-­CN-­0 04* Vegetation Database of the 485 Zhiyao Tang Northern Mountains in China AS-­CN-­0 05* Database Steppe Vegetation of 129 Kohei Suzuki Xinjiang AS-­EG-­0 01 Vegetation Database of Sinai in 926 Mohamed Z. Hatim Hatim (2012) Egypt AS-­ID-­0 01 Sulawesi Vegetation Database 24 Michael Kessler AS-­IR-­0 01 Vegetation Database of Iran 2,335 Jalil Noroozi Parastoo Mahdavi AS-­KG-­0 01 Vegetation Database of 452 Peter Borchardt Udo Schickhoff Borchardt and South-­Western Kyrgyzstan Schickhoff (2012) AS-­K Z-­0 01 Database of Meadow Vegetation 94 Viktoria Wagner Wagner (2009) in the NW Tian Shan Mountains AS-­MN-­0 01 Southern Gobi Protected Areas 1,516 Henrik von Wehrden Karsten Wesche von Wehrden, Database Wesche, and Miehe (2009) AS-­RU-­0 01 Wetland Vegetation Database of 2,381 Victor Chepinoga Chepinoga (2012) Baikal Siberia (WETBS) AS-­RU-­0 02 Database of Siberian Vegetation 9,116 Andrey Korolyuk Andrei Zverev (DSV) AS-­RU-­0 04 Database of the University of 445 Norbert Hölzel Wanja Mathar Münster -­ Biodiversity and Ecosystem Research Group’s Vegetation Research in Western Siberia and Kazakhstan AS-­SA-­0 01* Vegetation Database of Saudi 919 Mohamed Abd Arabia El-­Rouf Mousa El-­Sheikh AS-­TJ-­0 01 Eastern Pamirs 282 Kim André Vanselow Vanselow (2016) AS-­T W-­0 01 National Vegetation Database of 930 Ching-­Feng Li Chang-­Fu Hsieh Taiwan AS-­YE-­0 01 Socotra Vegetation Database 396 Michele De Sanctis Fabio Attorre De Sanctis and Attorre (2012) AU-­AU-­0 02 TERN AEKOS 21,261 Anita Smyth Ben Sparrow Turner, Smyth, Walker, and Lowe (2017) AU-­NC-­0 01 New Caledonian Plant Inventory 201 Jérôme Munzinger Philippe Birnbaum Ibanez et al. (2014) and Permanent Plot Network (NC-­PIPPN) AU-­NZ-­0 01 New Zealand National 1,895 Susan Wiser Wiser, Bellingham, Vegetation Databank and Burrows (2001) AU-­PG-­0 01 Forest Plots from Papua New 63 Timothy Whitfeld George Weiblen Whitfeld et al. (2014) Guinea (Continues) BRUELHEIDE et al. Journal of Vegetation Science | 173 TA B L E 2 (Continued) # of plots in GIVD ID Database name sPlot 2.1 Custodian Deputy custodian Reference NA-­0 0-­0 02 Tree Biodiversity Network 1,757 Luis Cayuela Cayuela et al. (2012) (BIOTREE-­NET) NA-­C A-­0 03 Database of Timberline 110 Viktoria Wagner Toby Spribille Wagner, Spribille, Vegetation in NW North Abrahamczyk, and America Bergmeier (2014) NA-­C A-­0 04 Understory of Sugar Maple 156 Isabelle Aubin Aubin, Gachet, Dominated Stands in Quebec Messier, and and Ontario (Canada) Bouchard (2007) NA-­C A-­0 05* Boreal Forest of Canada 89 Yves Bergeron Louis De Grandpré NA-­GL-­0 01 Vegetation Database of 664 Birgit Jedrzejek Fred J.A. Daniëls Sieg, Drees, and Greenland Daniëls (2006) NA-­US-­0 02 VegBank 67,352 Robert K. Peet Michael T. Lee Peet et al. (2012) NA-­US-­0 06 Carolina Vegetation Survey 17,221 Robert K. Peet Michael T. Lee Peet et al. (2012) Database NA-­US-­014 Alaska-­Arctic Vegetation Archive 1,363 Donald A. Walker Amy Breen Walker et al. (2016) SA-­0 0-­0 02 VegPáramo 2,643 Gwendolyn Peyre Xavier Font Peyre et al. (2015) SA-­AR-­0 02 Vegetation Database of Central 218 Marcelo R. Cabido Alicia Acosta Argentina SA-­BO-­0 03 Bolivia Forest Plots 75 Michael Kessler Sebastian Herzog SA-­BR-­0 02 Forest Inventory, State of Santa 1,669 Alexander Christian André Luis de Gasper Vibrans, Sevegnani, Catarina, Brazil (IFFSC Project) Vibrans Lingner, de Gasper, and Sabbagh (2010) SA-­BR-­0 03 Grasslands of Rio Grande do Sul, 320 Eduardo Vélez-­Martin Valério De Patta Pillar Brazil SA-­BR-­0 04 Grassland Database of Campos 161 Gerhard E. Overbeck Valério De Patta Pillar Sulinos SA-­CL-­0 02 SSAForests_Plots_db 261 Alvaro G. Gutierrez SA-­CL-­0 03* Chilean Park Transects 165 Aníbal Pauchard Alicia Marticorena Pauchard, Fuentes, -­ Fondecyt 1040528 Jiménez, Bustamante, and Marticorena (2013) SA-­EC-­0 01 Ecuador Forest Plot Database 172 Jürgen Homeier Note. GIVD ID refers to the ID in the Global Index of Vegetation-­Plot Databases (http://www.givd.info), which manages the metadata for sPlot and provides updated online descriptions of these databases; * after the GIVD ID indicates that the respective database description is currently not visible on the GIVD website. Datasets contributed in harmonized format from a continental data aggregator (“collective database” according to the sPlot Rules) are listed under its name. Further references, attributions and disclaimers for particular datasets are found Appendix S1. >25%. Similarly, we defined non-­forests by calculating the cover of 2.4 | Phylogenetic information all taxa that were not defined as trees or shrubs (also taken from the TRY plant growth form information) and that were not taller than We developed a workflow to generate a phylogeny of the vascu- 2 m, using the TRY data on mean plant height. In total, 21,888 taxa lar plant species in sPlot, using the phylogeny of Zanne et al. (2014), belonged to this category. We defined all plots as non-­forests if the updated by Qian and Jin (2016). Species present in sPlot but miss- sum of relative cover of these low-­stature, non-­tree and non-­shrub ing from this phylogeny were added next to a randomly selected taxa was >90%. As we did not have the growth form and height in- congener (see also Maitner et al., 2018). This approach has been formation for all taxa, a fraction of about 25% of the plots remained demonstrated to introduce less bias into subsequent analyses than unassigned (i.e., neither forest, nor non-­forest). In addition, more adding missing species as polytomies to the respective genera detailed classifications of plots into physiognomic formations (Table (Davies, Kraft, Salamin, & Wolkovich, 2012). We only added species S3.2 in Appendix S3) and naturalness (Table S3.3 in Appendix S3) based on taxonomic information on the genus level, thus not mak- were derived from various types of plot-­level or database-­level in- ing use of family affiliation. Because of the absence of congeners in formation provided by the sources and stored in five separate fields the reference phylogeny, 7,147 species could not be added (11.7% (see Table S2.1 in Appendix S2). of all resolved taxa in sPlot and TRY). This resulted in a phylogeny 174 | Journal of Vegetation Science BRUELHEIDE et al. with 54,067 resolved taxon names from 61,214 standardized taxa calculated growing degree days for 1°C (GDD1) and 5°C (GDD5), in the combined list of sPlot and TRY. The tree was finally pruned according to Synes and Osborne (2011) and based on CHELSA data, to the vascular plant taxa of the current sPlot version 2.1, resulting and included the index of aridity and potential evapotranspiration in a phylogenetic tree for 53,489 out of the 58,066 taxa in sPlot. Of extracted from the CGIAR-­C SI website (www.cgiar-csi.org). In addi- these 53,489 names, 16,026 are also found among the 31,389 taxa tion, we extracted seven soil variables from the SOILGRIDS project in the phylogenetic tree of Qian and Jin (2016), i.e., 51.1%. The full (https://soilgrids.org/; licensed by ISRIC – World Soil Information), procedure and the R code are available in Purschke (2017b). downloaded at 250-­m resolution and then converted to the same 30-­arc second grid format of CHELSA. To explore the distribution of sPlot data in the global environmental space, we subjected all 30 2.5 | Associated environmental plot information climate and soil variables of the global terrestrial surface rasterized To complement the plot data, we harmonized geographical coor- on a 2.5 arc-­minute grid resolution to a principal component analysis dinates (in decimal degrees), elevation (m above sea level), aspect (PCA) on standardized and centered data. We subsequently created (degrees) and slope (degrees) as provided by the contributing data- a grid of 100 cells × 100 cells within the bi-­dimensional environ- bases. All other variables were too sparsely and too inconsistently mental space defined by the first two PCA axes (PC1 and PC2) and sampled across databases to be combined in the global set, but were counted the number of terrestrial cells per environmental grid cell of retained in the original data sources and can be retrieved for par- the PC1–PC2 space. Then, we counted the number of plots in sPlot ticular purposes. in the same PCA grid (Figure 2). We used the geographic coordinates to create a geodatabase in We linked all vegetation plots to two global biome classifica- ArcGIS 14.1 (ESRI, Redlands, CA) to link sPlot 2.1 to these climate tions. We used the World Wildlife Fund (WWF) spatial informa- and soil data. We retrieved data for all the 19 bioclimatic variables tion on terrestrial ecoregions (Olson et al., 2001) to assign plots provided by CHELSA v1.1 (Karger et al., 2017) by averaging climatic to one of the 867 ecoregions, 14 biomes and eight biogeographic data from the period 1979–2013 at 30 arc seconds (about 1 km in realms. The WWF approach is based on a bottom-­up expert sys- grid cells near to the equator). These variables are the same as the tem using various regional biodiversity sources to define ecore- ones used in WorldClim (www.worldclim.org; Hijmans, Cameron, gions, which in turn are grouped into realms and biomes (Olson Parra, Jones, & Jarvis, 2005), but calculated with a downscaling et al., 2001). In addition, we created a shapefile for the ecozones approach based on estimates of the ERA-­Interim climatic reanaly- defined by Schultz (2005) to represent major biomes in response to sis (Dee et al., 2011). While the CHELSA climatological data have a global climatic variation. Since these zones are climatically hetero- similar accuracy as other products for temperature, they are more geneous in mountain regions, we differentiated an additional “al- precise for precipitation patterns (Karger et al., 2017). We also pine” biome for mountain areas above the lower mountain thermal F I G U R E 2 Distribution of vegetation plots from sPlot 2.1 in the global environmental space. Comparison of the distribution of all terrestrial 2.5 arc-­minute cells (a) and plots in sPlot 2.1 (b) in the principal component analysis (PCA) space defined on 30 environmental (climate and soil) variables. The PCA space was divided into a 100 × 100 regular grid. For each element of this grid, the graphs show the number of 2.5 arc-­minute cells (a) and plots (b), respectively. Colors refer to the logarithm of number of plots, with the legend showing untransformed number of plots. The first and second PCA axis explained 48.6% and 27.3% of the total variance BRUELHEIDE et al. Journal of Vegetation Science | 175 belt, as defined in the classification of world mountain regions by 3 | CO NTE NT O F S PLOT 2 .1 Körner et al. (2017). This resulted in a distinction of 10 major bi- omes (Figure S4.5 in Appendix S4), whose shapefile is freely avail- 3.1 | Plot community data able (Appendix S5). sPlot 2.1 contains 1,121,244 vegetation plots from 160 countries and from all continents (Figure 3). The global coverage is biased 2.6 | Trait information towards Europe, North America and Australia, reflecting unequal sampling effort across the globe (Table 1). At the ecoregion level, To broaden the potential applications of the global vegetation da- major gaps occur in the wet tropics of South America and Asia, as tabase in functional contexts, we linked sPlot to TRY. We accessed well as in subtropical deserts worldwide and in the North American plant trait data from TRY version 3.0 on August 10, 2016, and in- taiga. Although the plots are highly clustered geographically, their cluded 18 traits that describe the leaf, wood and seed economics coverage in the environmental space is much more representative: spectra (Westoby, 1998; Reich, 2014; Table S6.4 in Appendix S6), the highest concentration of plots is found in environments that are and are known to affect different key ecosystem processes and to most abundant globally (Figure 2), while they are lacking in the very respond to macroclimatic drivers. These traits were represented moist parts of the environmental space, which are also spatially rare, across all species in the TRY database by at least 1,000 trait re- and in the very cold parts, which are sparsely vegetated. cords. We excluded trait records from manipulative experiments In most cases (98.4%), plot records in sPlot include full species and outliers (Kattge et al., 2011), which resulted in a matrix with lists of vascular plants, while 1.6% had only wood species above 632,938 individual plant records on 52,032 taxa in TRY, having a certain diameter or only the most dominant species recorded. data records for an average of 3.08 of the 18 selected traits. On Terricolous bryophytes and lichens were additionally identified average, each trait has been measured at least once in 17.1% of all in 14% and 7% of plots, respectively (Table S2.1 in Appendix taxa. In order to attain data for these 18 traits for all species with S2). Forest and non-­forest plots comprise 330,873 (29.7%) and at least one trait value in TRY, we employed hierarchical Bayesian 513,035 (46.0%) of all plots in sPlot, respectively. In most cases, modeling, using the R package ‘BHPMF’ (Fazayeli, Banerjee, Kattge, species abundance was estimated using different variants of the Schrodt, & Reich, 2017; Schrodt et al., 2015), to fill a gap in the Braun-­B lanquet cover–abundance scale (66%), followed by per- matrix of individual plant records in TRY. Gap filling allows obtain- centage cover (15%) and 55 other numeric or ordinal scales. The ing trait values for a species on which this trait has not been meas- temporal extent of the data spans from 1885 to 2015, but >94% ured, but for which other traits are available. To assess gap-­f illing of vegetation plots were recorded later than 1960 (Figure S2.1 quality, we used the probability density distributions provided by in Appendix S2). Almost all plots are georeferenced (1,120,686) BHPMF for each imputation and removed highly uncertain imputa- and the majority of plots have location uncertainty of 10 m or less tions with a coefficient of variation >1. We then loge-­transformed (Figure S2.2 in Appendix S2). all gap-­f illed trait values and averaged each trait by taxon. For taxa Vascular plant richness per plot ranges from 1 to 723 species recorded at genus level only, we calculated genus means, result- (median = 17 species). The most frequent richness class is between ing in a full trait matrix for 26,632 out of the 54,519 taxa in sPlot 20 and 25 species (Figure S2.3 in Appendix S2). Plot size is reported (45.9%), with 6, 1,510 and 25,116 taxa at the family, genus and spe- in 65.4% of plots, ranging from <1 m2 to 25 ha, with a median of cies level, respectively. These species covered 88.7% of all species-­ 36 m2. While forest plots have plot sizes ≥100 m2, and in most cases by-­plot combinations. ≤1,000 m2, non-­forest plots range between 5 and 100 m2 (Figure For every trait j and plot k, we calculated the community-­weighted S2.4 in Appendix S2). When using these size ranges, forest plots mean (CWM) and the community-­weighted variance (CWV) for each tend to be richer in species (Figure 4a). The fact that the gradient of the 18 traits in a plot (Enquist et al., 2015): in richness found in our plots was at least one order of magnitude nk ∑ stronger than differences that could be expected by the differ- CWMj,k = pi,k ti,j i ences in plot size prompted us to produce the first global maps of plot-­scale species richness, separately for forests and non-­forests (Figure 4a). While plots with complete vascular species composition nk CWVj,k = ∑ pi,k (ti,j − CWMj,k )2 are largely lacking from the wet tropics, for the remaining biomes i the plot-­scale richness data do not show the typical latitudinal rich- where nk is the number of species with trait information in plot k, ness gradient in either formation. Particularly species-­rich forests pi,k is the relative abundance of species i in plot k calculated as the are found in the wet subtropics (such as SE United States, Taiwan species’ fraction in cover or abundance of total cover or abundance, and the East coast of Australia) as well as in some mountainous re- and ti,j is the mean value of species i for trait j. CWMs and CWVs gions of the nemoral and steppic biomes of Eurasia. Likewise, non-­ were calculated for 18 traits in 1,117,369 and 1,099,463 plots, re- forest communities have a particularly high mean vascular plant spectively, the second being a smaller number as at least two taxa species in mountainous regions of the nemoral and steppic biomes were needed for CWV calculation. of Eurasia. 176 | Journal of Vegetation Science BRUELHEIDE et al. F I G U R E 3 Global coverage of sPlot 2.1. (a) Contributing databases identified by different colours with indication of the two data aggregators (EVA, TAVA) and a few particularly large individual databases; (b) available plot numbers per WWF Ecoregion; and (c) available plot density in grid cells of 100 km × 100 km BRUELHEIDE et al. Journal of Vegetation Science | 177 F I G U R E 4 Examples of global community-­level patterns that can be derived from (a) sPlot alone and (b–d) sPlot combined with TRY, here shown as raw data averaged by 1-­degree grid cells. There are only a very few cells (142 out of 2633) comprising only a single plot. For the maps, only plots with full vascular species composition and spatial accuracy <5 km were used. They are based on 148,474 and 218,051 plots for forests and non-­forests, respectively. Note that these maps are not corrected for biases caused by the facts that not all community types were recorded in all grid cells and that plot sizes as well as the fraction of species with available trait data varied spatially. Maps show patterns of (a) fine-­grain alpha diversity, expressed as vascular plant species richness (only plots with plot sizes of 100–1000 m² for forests and 5–100 m² for non-­forests); (b) community-­weighted means (CWMs) for loge-­transformed trait values of specific leaf area (SLA, m2/kg); (c) plant height (m); and (d) seed mass (mg) 178 | Journal of Vegetation Science BRUELHEIDE et al. ensures that the same data can be used in parallel projects and that 3.2 | Phylogenetic information the data underlying a specific study remain accessible in the future, The phylogenetic tree for sPlot was produced from 53,489 vascu- thus allowing re-­analysis. Each new version will be matched to the lar plant names contained in the database, comprising 5518 genera current TRY database. (Appendix S7). Moderately to highly frequent species in sPlot 2.1 are Data access to sPlot is regulated by the Governance and Data equally distributed across the phylogeny (corresponding to yellow- Property Rules (www.idiv.de/sPlot) to ensure a fair balance between ish to reddish colors for low and high peaks, respectively, in Figure the interests of data contributors and data analysts. In brief, the S7.6 in Appendix S7). Coverage of species included in the phylog- sPlot Rules state that: (a) all contributing vegetation-­plot databases eny ranges from 89% of species that occur only once in all plots become members of the sPlot consortium, represented by their cus- to 100% of species with a frequency >10,000 plots (Figure S7.7 in todian and deputy custodian; (b) vegetation-­plot data contributed Appendix S7). to sPlot remain the property of the data contributors and can be withdrawn at any time except for approved projects; (c) other sci- entists (e.g., data managers or participants of the sPlot workshops) 3.3 | Functional information with particular responsibilities may also be appointed as personal The proportion of species with trait information increases with the members to the sPlot consortium; (d) sPlot data can be requested species’ frequency in plots. Gap-­filled trait information is available for projects that involve at least one member of the sPlot consor- for 77.2% and 96.2% for taxa that occurred in more than 100 and tium; (e) whenever a project has been proposed, all sPlot consortium 1,000 plots, respectively. Trait coverage is similar across biomes members will be informed and can declare their interest in becoming (Figure S8.8 in Appendix S8). Across all biomes, the proportion of co-­authors of manuscripts resulting from this project and then be- species for which gap-­filled trait data are available increases with the coming actively involved in data evaluation and writing; and (f) if also species’ frequency across plots. Compared to gap-­filled data, trait the matched gap-­filled or original trait data from TRY are requested coverage for the original trait data is considerably lower, being high- for a project, likewise members from the TRY consortium can opt-­in est for height, seed mass, leaf area and specific leaf area (SLA, Figure as co-­authors. The sPlot database is, therefore, available according S8.9 in Appendix S8). to a ‘give-­and-­receive’ system. Moreover, the data are available to The high representation of the 18 traits in the gap-­filled trait data any researcher by establishing a collaboration that includes and is and the high degree of trait coverage for frequent species across all supported by at least one sPlot consortium member. biomes (>75%) made us confident to produce the first maps of global The sPlot consortium is governed by a Steering Committee patterns of community-­weighted means (CWMs) (Figure 4b–d). The elected by all consortium members for two-­year, renewable terms. maps show the main trait dimensions of SLA, height and seed mass, Project proposals can be submitted to the Steering Committee, separately for forests and non-­forests, for those regions of the world which ensures that the sPlot Rules are followed and redundant work that are already sufficiently covered by sPlot data. Accordingly, between overlapping projects is avoided. The lists of databases, CWMs of SLA are quite similar for forest and non-­forest plots, being sPlot consortium members and the Steering Committee members highest in western North America and Europe and lowest in east- are updated regularly on the sPlot website, as are the sPlot Rules and ern North America, East and South Australia (Figure 4b). Non-­forest the list of approved projects. vegetation shows lowest CWMs of SLA in the desert regions of the Namib and Sinai. Forests with highest CWMs of canopy height are found along the western and eastern coast of North America, 5 | E X PEC TE D I M PAC T A N D LI M ITATI O N S some regions in Europe, East Asia and southern Australia (Figure 4c). These areas only partly coincide with those of highest seed masses The main aim of the sPlot database is to catalyze a collaborative for forests, while seed mass in non-­forests is highest in the east- network for understanding global diversity patterns of plant com- ern Mediterranean Basin and in Central Asia (Figure 4d). The corre- munities in space and time. sPlot provides a unique, integrated sponding patterns for CWV are shown in Figure S9.10 in Appendix global repository of data that would otherwise be fragmented in S9. unconnected and structurally inconsistent databases at regional, national or continental levels. Together with the provision of har- monized phylogenetic, functional and environmental information, 4 | DATA U SAG E sPlot allows, for the first time, global analyses of plant community data. Compared to approaches using data aggregated from species The sPlot database (the vegetation-­plot data, including the environ- occurrences in grid cells, sPlot will significantly advance ecological mental information for each plot and the species phylogeny) is re- analyses and future interdisciplinary research in at least four differ- leased in fixed versions to allow reproducibility of results, but also ent ways. due to the enormous effort needed for data integration and harmo- nization and for updating the phylogeny. By delivering few fixed ver- 1. Using sPlot, one can predict the species that can co-exist in sions while keeping older versions available, the sPlot consortium a community and also the frequencies of their co-occurrence BRUELHEIDE et al. Journal of Vegetation Science | 179 (Breitschwerdt, Jandt, & Bruelheide, 2015) or niche overlap trait data for a (small) portion of the species and by the lack of plot-­ (Broennimann et al., 2012). In addition, emerging tools such as specific trait measures. Furthermore, the non-­random and geographi- Markov networks can be used to infer strengths of interspecific cally and ecologically very unequal distribution of the plots contained interactions (Harris, 2016). When investigating community as- in sPlot call for stratified resampling to balance records of different sembly rules, the same information can be used to derive environments (e.g., stratified by climate, Figure 2) or physiognomic for- species pools for specific vegetation types (de Bello et al., 2016; mations (Figure 4). Users of sPlot need to be aware of these and other Karger et al., 2016; Lewis, Szava-Kovats, & Pärtel, 2016). limitations and to correct potential biases for their specific research Moreover, the co-occurrence data from sPlot can be used to question. address fundamental patterns and drivers of plant invasions better than information on large geographic entities (e.g., van Kleunen et al., 2015) alone could. 6 | CO N C LU S I O N 2. sPlot data can be aggregated across all types of plots, by grid cells, ecoregions, environment, or even vegetation type or forma- sPlot is a unique global database of plant community records sam- tion. Furthermore, replicated plots within grid cells, ecoregions, or pled with relatively similar methods widely used in vegetation ecol- any other subdivision of environmental conditions or vegetation ogy. The integration of co-­occurrence data into a unified database types allow users to derive measures of compositional differences that can be directly linked to environmental, functional and phyloge- between plant communities within grid cells (= beta diversity; netic information, makes sPlot an unprecedented and essential tool Table 1). Thus, the community data are an important complement for analyzing global plant diversity, the structure of plant commu- to regional-scale species occurrence data (e.g., Enquist et al., nities and the co-­occurrence of plant species. The compatibility of 2016; Kreft & Jetz, 2007). this consolidated database with other global databases, e.g., via a 3. sPlot data provide information on the proportion of species in a joint taxonomic backbone with TRY and the Global Naturalized Alien community (in terms of cover, basal area, frequency). When com- Flora (GloNAF; van Kleunen et al., 2015) (via taxon names), or via bined with functional trait information, relative abundance of spe- standardized geo-­reference with databases of environmental infor- cies allows calculation of community abundance-weighted mean mation such as CHELSA, WorldClim or SoilGrids (Bruelheide et al., trait values (Bruelheide et al., 2018). Information on the relative 2018), facilitates data integration and creates new research oppor- contribution of species to a community-aggregated trait value is tunities. The adaptive management of the database employed by the particularly necessary when traits are used as proxies for vegeta- sPlot consortium allows regular incorporation of new data, resulting tion functions and processes, allowing to test, among other things, in a dynamic platform for storing and analyzing the most compre- the mass ratio hypothesis (Garnier et al., 2004; Grime, 1998) and hensive compilation of plant community data worldwide. to assess the roles of divergent traits (Díaz et al., 2007; Kröber et al., 2015). AC K N OW L E D G E M E N T S 4. Plant species within plots can be linked to traits that predict inter- actions with organisms from other trophic groups, both below- We are grateful to thousands of vegetation scientists who sam- ground (mycorrhizae, soil decomposers) and above-ground pled vegetation plots in the field or digitized them into regional, (herbivores and pollinators). This will allow linking vegetation plot national or international databases. We also appreciate the sup- information to ecosystem processes and services such as pest port of the German Research Foundation for funding sPlot as one control, pollination and nutrient cycling (e.g., de Bello et al., 2010). of the iDiv (DFG FZT 118) research platforms, and the organization of three workshops through the sDiv calls. We acknowledge this Despite the large amount of available data and its potential suit- support with naming the database “sPlot”, where the “s” refers to ability for global research, a number of limitations must be considered the sDiv synthesis workshops. The study was supported by the by future users of sPlot, such as (a) biases towards certain regions and TRY initiative on plant traits (http://www.try-db.org). For all fur- communities, (b) near-­complete lack of plots with complete vascular ther acknowledgements see Appendix S10. We thank Meelis Pärtel plant species composition for certain regions (e.g., the wet tropics), for his very fast and constructive feedback on an earlier version of (c) identification or sampling errors by the surveyors and incomplete this manuscript. records because the detection of some species may be precluded in certain seasons by their phenology, (d) taxonomic uncertainty, particu- AU T H O R C O N T R I B U T I O N S larly in the tropics, (e) strongly varying plot sizes employed in different studies and regions, (f) lack of trait measures at the plot level. For ex- H.Bru. had the original idea and led the consortium from the start, ample, patterns of diversity components are typically affected by grain while O.Pu. and J.D. coordinated the sPlot workshops. J.D., S.M.H. size. This means that using sPlot data for such studies either requires and U.J. compiled the databases to be included in sPlot. J.D. and later filtering for plots with identical or at least similar size or accounting B.J.-­A . and F.M.S. coordinated the network and the database. O.P. for the plot-­size effects in the statistical model. In addition, analyses prepared the taxonomic and phylogenetic data. S.M.H programmed of functional diversity with sPlot data are limited by the absence of the Turboveg software. B.Sa., F.J., H.Bru., J.D., J.K., M.Ch., and V.D.P. 180 | Journal of Vegetation Science BRUELHEIDE et al. organized the network in the Steering Committee. B.J.-­A . and H.Bru. Luis Cayuela https://orcid.org/0000-0003-3562-2662 led the writing together with J.D. and input from S.M.H., O.Pu., Tomáš Černý https://orcid.org/0000-0003-2637-808X M.Ch., F.J., J.K., V.D.P., B.Sa., I.Au., I.B., R.K.P., R.F., S.H., U.J., J.L., G.P., F.M.S., M.S., F.S. and M.W. The rest of authors (ordered alpha- Victor Chepinoga https://orcid.org/0000-0003-3809-7453 betically) contributed the plot and trait data. All authors agreed with János Csiky https://orcid.org/0000-0002-7920-5070 the final manuscript. Els De Bie https://orcid.org/0000-0001-7679-743X Michele De Sanctis https://orcid.org/0000-0002-7280-6199 DATA AC C E S S I B I L I T Y Jaime Fagúndez https://orcid.org/0000-0001-6605-7278 Xavier Font https://orcid.org/0000-0002-7253-8905 The data contained in sPlot (the vegetation-­plot data complemented by species phylogeny and environmental information) are available on Estelle Forey https://orcid.org/0000-0001-6082-3023 request, through contacting any of the consortium members for sub- André Luis Gasper https://orcid.org/0000-0002-1940-9581 mitting a paper proposal. The proposals should follow the Governance Alvaro G. Gutierrez https://orcid.org/0000-0001-8928-3198 and Data Property Rules of the sPlot Working Group, which are avail- Tianhua He https://orcid.org/0000-0002-0924-3637 able on the sPlot website (www.idiv.de/sPlot). After acceptance, the respective data will be provided. In addition to the plot data, CWMs Pedro Higuchi https://orcid.org/0000-0002-3855-555X and CWVs of 18 plant traits are available for every plot. Norbert Hölzel https://orcid.org/0000-0002-6367-3400 Steven Jansen https://orcid.org/0000-0002-4476-5334 ORCID Martin Jiroušek https://orcid.org/0000-0002-4293-478X Norbert Jürgens https://orcid.org/0000-0003-3211-0549 Helge Bruelheide https://orcid.org/0000-0003-3135-0356 Ali Kavgacı https://orcid.org/0000-0002-4549-3668 Jürgen Dengler https://orcid.org/0000-0003-3221-660X Elizabeth Kearsley https://orcid.org/0000-0003-0046-3606 Borja Jiménez-Alfaro https://orcid.org/0000-0001-6601-9597 Michael Kessler https://orcid.org/0000-0003-4612-9937 Oliver Purschke https://orcid.org/0000-0003-0444-0882 Ingolf Kühn https://orcid.org/0000-0003-1691-8249 Milan Chytrý https://orcid.org/0000-0002-8122-3075 Flavia Landucci https://orcid.org/0000-0002-6848-0384 Valério D. Pillar https://orcid.org/0000-0001-6408-2891 Ching-Feng Li https://orcid.org/0000-0003-0744-490X Jens Kattge https://orcid.org/0000-0002-1022-8469 Peter Manning https://orcid.org/0000-0002-7940-2023 Idoia Biurrun https://orcid.org/0000-0002-1454-0433 Corrado Marcenò https://orcid.org/0000-0003-4361-5200 Richard Field https://orcid.org/0000-0003-2613-2688 Maurizio Mencuccini https://orcid.org/0000-0003-0840-1477 Jonathan Lenoir https://orcid.org/0000-0003-0638-9582 Vanessa Minden https://orcid.org/0000-0002-4933-5931 Robert K. Peet https://orcid.org/0000-0003-2823-6587 Jesper Erenskjold Moeslund https://orcid. Francesco Maria Sabatini https://orcid. org/0000-0001-8591-7149 org/0000-0002-7202-7697 Marco Moretti https://orcid.org/0000-0002-5845-3198 Marco Schmidt https://orcid.org/0000-0001-6087-6117 Jérôme Munzinger https://orcid.org/0000-0001-5300-2702 Franziska Schrodt https://orcid.org/0000-0001-9053-8872 Ülo Niinemets https://orcid.org/0000-0002-3078-2192 Emiliano Agrillo https://orcid.org/0000-0003-2346-8346 Arkadiusz Nowak https://orcid.org/0000-0001-8638-0208 Miguel Alvarez https://orcid.org/0000-0003-1500-1834 Gerhard E. Overbeck https://orcid.org/0000-0002-8716-5136 Pierangela Angelini https://orcid.org/0000-0002-5321-9757 Wim A. Ozinga https://orcid.org/0000-0002-6369-7859 Mohammed A. S. Arfin Khan https://orcid. org/0000-0001-6275-7023 Hristo Pedashenko https://orcid.org/0000-0002-6743-0625 Fabio Attorre https://orcid.org/0000-0002-7744-2195 Josep Peñuelas https://orcid.org/0000-0002-7215-0150 Michael Beckmann https://orcid.org/0000-0002-5678-265X Aaron Pérez-Haase https://orcid.org/0000-0002-5974-7374 Yves Bergeron https://orcid.org/0000-0003-3707-3687 Petr Petřík https://orcid.org/0000-0001-8518-6737 Erwin Bergmeier https://orcid.org/0000-0002-6118-4611 Oliver L. Phillips https://orcid.org/0000-0002-8993-6168 Zoltán Botta-Dukát https://orcid.org/0000-0002-9544-3474 Cyrus Samimi https://orcid.org/0000-0001-7001-7893 Chaeho Byun https://orcid.org/0000-0003-3209-3275 Jozef Šibík https://orcid.org/0000-0002-5949-862X Laura Casella https://orcid.org/0000-0003-2550-3010 Željko Škvorc https://orcid.org/0000-0002-3052-699X BRUELHEIDE et al. Journal of Vegetation Science | 181 Boyle, B., Hopkins, N., Lu, Z., Raygoza Garay, J. 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S U P P O R T I N G I N FO R M AT I O N information system (GIS) software) and accompanying accessory files (database, projection etc.) Additional supporting information may be found online in the Appendix S6 Trait information in sPlot 2.1 Supporting Information section at the end of the article. Appendix S7 Phylogenetic information in sPlot 2.1 Appendix S8 Gap-­filled trait information Appendix S1 Additional references, attributions and disclaimers for Appendix S9 Global patterns of community-­weighted variances datasets included in sPlot 2.1 Appendix S10 Detailed acknowledgements Appendix S2 Data associated to the vegetation plot records stored in sPlot 2.1 Appendix S3 Details on the workflow for setting up plot definitions How to cite this article: Bruelheide H, Dengler J, Jiménez- in sPlot 2.1 Alfaro B, et al. sPlot – A new tool for global vegetation Appendix S4 Biome classification created for sPlot 2.1 analyses. J Veg Sci. 2019;30:161–186. https://doi.org/10.1111/ Appendix S5 Zip file of the biome classification of Appendix S4 jvs.12710 containing the shapefile (Geospatial vector data for geographic