grassplot – a database of multi-scale plant diversity in palaearctic grasslands -...

GrassPlot – a database of multi-scale plant diversity in Palaearctic grasslands

Jürgen Dengler*, Viktoria Wagner, Iwona Dembicz, Itziar García-Mijangos, Alireza Naqinezhad, Steffen Boch, Alessandro Chiarucci, Timo Conradi, Goffredo Filibeck, Riccardo Guarino, Monika Janišová, Manuel J. Steinbauer, Svetlana Aćić, Alicia T.R. Acosta, Munemitsu Akasaka, Marc-Andre Allers, Iva Apostolova, Irena Axmanová, Branko Bakan, Alina Baranova, Manfred Bardy-Durchhalter, Sándor Bartha, Esther Baumann, Thomas Becker, Ute Becker, Elena Belonovskaya, Karin Bengtsson, José Luis Benito Alonso, Asun Berastegi, Ariel Bergamini, Ilaria Bonini, Hans Henrik Bruun, Vasyl Budzhak, Alvaro Bueno, Juan Antonio Campos, Laura Cancellieri, Marta Carboni, Cristina Chocarro, Luisa Conti, Marta Czarniecka-Wiera, Pieter De Frenne, Balázs Deák, Yakiv P. Didukh, Martin Diekmann, Christian Dolnik, Cecilia Dupré, Klaus Ecker, Nikolai Ermakov, Brigitta Erschbamer, Adrián Escudero, Javier Etayo, Zuzana Fajmonová, Vivian A. Felde, Maria Rosa Fernández Calzado, Manfred Finckh, Georgios Fotiadis, Mariano Fracchiolla, Anna Ganeva, Daniel García-Magro, Rosario G. Gavilán, Markus Germany, Itamar Giladi, François Gillet, Gian Pietro Giusso del Galdo, Jose M. González, John-Arvid Grytnes, Michal Hájek, Petra Hájková, Aveliina Helm, Mercedes Herrera, Eva Hettenbergerová, Carsten Hobohm, Elisabeth M. Hüllbusch, Nele Ingerpuu, Ute Jandt, Florian Jeltsch, Kai Jensen, Anke Jentsch, Michael Jeschke, Borja Jiménez-Alfaro, Zygmunt Kącki, Kaoru Kakinuma, Jutta Kapfer, Ali Kavgacı, András Kelemen, Kathrin Kiehl, Asuka Koyama, Tomoyo F. Koyanagi, Łukasz Kozub, Anna Kuzemko, Magni Olsen Kyrkjeeide, Sara Landi, Nancy Langer, Lorenzo Lastrucci, Lorenzo Lazzaro, Chiara Lelli, Jan Lepš, Swantje Löbel, Arantzazu L. Luzuriaga, Simona Maccherini, Martin Magnes, Marek Malicki, Corrado Marcenò, Constantin Mardari, Leslie Mauchamp, Felix May, Ottar Michelsen, Joaquín Molero Mesa, Zsolt Molnár, Ivan Y. Moysiyenko, Yuko K. Nakaga, Rayna Natcheva, Jalil Noroozi, Robin J. Pakeman, Salza Palpurina, Meelis Pärtel, Ricarda Pätsch, Harald Pauli, Hristo Pedashenko, Robert K. Peet, Remigiusz Pielech, Nataša Pipenbaher, Chrisoula Pirini, Zuzana Plesková, Mariya A. Polyakova, Honor C. Prentice, Jennifer Reinecke, Triin Reitalu, Maria Pilar Rodríguez-Rojo, Jan Roleček, Vladimir Ronkin, Leonardo Rosati, Ejvind Rosén, Eszter Ruprecht, Solvita Rusina, Marko Sabovljević, Ana María Sánchez, Galina Savchenko, Oliver Schuhmacher, Sonja Škornik, Marta Gaia Sperandii, Monika Staniaszek-Kik, Zora Stevanović-Dajić, Marin Stock, Sigrid Suchrow, Laura M.E. Sutcliffe, Grzegorz Swacha, Martin Sykes, Anna Szabó, Amir Talebi, Cătălin Tănase, Massimo Terzi, Csaba Tölgyesi, Marta Torca, Péter Török, Béla Tóthmérész, Nadezda Tsarevskaya, Ioannis Tsiripidis, Rossen Tzonev, Atushi Ushimaru, Orsolya Valkó, Eddy van der Maarel, Thomas Vanneste, Iuliia Vashenyak, Kiril Vassilev, Daniele Viciani, Luis Villar, Risto Virtanen, Ivana Vitasović Kosić, Yun Wang, Frank Weiser, Julia Went, Karsten Wesche, Hannah White, Manuela Winkler, Piotr T. Zaniewski, Hui Zhang, Yaron Ziv, Sergey Znamenskiy & Idoia Biurrun

Abstract

GrassPlot is a collaborative vegetation-plot database organised by the Eurasian Dry Grassland Group (EDGG) and listed in the Global Index of Vegetation-Plot Databases (GIVD ID EU-00-003). GrassPlot collects plot records (relevés) from grasslands and other open habitats of the Palaearctic biogeographic realm. It focuses on precisely delimited plots of eight standard grain sizes (0.0001; 0.001; ... 1,000 m²) and on nested-plot series with at least four different grain sizes. The usage of GrassPlot is regulated through Bylaws that intend to balance the interests of data contributors and data users. The current version (v. 1.00) contains data for approximately 170,000 plots of different sizes and 2,800 nested-plot series. The key components are richness data and meta-data. However, most included datasets also encompass compositional data. About 14,000 plots have near-com-plete records of terricolous bryophytes and lichens in addition to vascular plants. At present, GrassPlot con-tains data from 36 countries throughout the Palaearctic, spread across elevational gradients and major grassland

© 2018 Gebrüder Borntraeger, 70176 Stuttgart, GermanyDOI: 10.1127/phyto/2018/0267

www.borntraeger-cramer.de0340-269X/2018/0267 $ 7.65

*Corresponding author’s address: Vegetation Ecology Group, Institute of Natural Resource Sciences (IUNR), Zurich University of Applied Sciences (ZHAW), Grüentalstr. 14, 8820 Wädenswil, Switzerland; [email protected]. Complete addresses of all authors can be found at the bottom of the paper.

Phytocoenologia Vol. 48 (2018), Issue 3, 331–347 Published online May 2018

Long Database Report

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332 Jürgen Dengler et al.

types. GrassPlot with its multi-scale and multi-taxon focus complements the larger international vegetation-plot databases, such as the European Vegetation Archive (EVA) and the global database “sPlot”. Its main aim is to facilitate studies on the scale- and taxon-dependency of biodiversity patterns and drivers along macroeco-logical gradients. GrassPlot is a dynamic database and will expand through new data collection coordinated by the elected Governing Board. We invite researchers with suitable data to join GrassPlot. Researchers with project ideas addressable with GrassPlot data are welcome to submit proposals to the Governing Board.

Keywords: biodiversity; European Vegetation Archive (EVA); Eurasian Dry Grassland Group (EDGG); grass-land vegetation; GrassPlot; macroecology; multi-taxon; nested plot, scale-dependence; species-area relationship (SAR); sPlot; vegetation-plot database.

Abbreviations: EDGG = Eurasian Dry Grassland Group; EVA = European Vegetation Archive; GrassPlot = Database of Scale-Dependent Phytodiversity Patterns in Palaearctic Grasslands; SAR = species-area relation-ship.

Submitted: 15 January 2018; first decision: 19 March 2018; accepted: 23 March 2018

Co-ordinating Editor: Florian Jansen

GIVD Fact Sheet

Information as of 2018-03-23 further details and future updates available from http://www.givd.info/ID/EU-00-003

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GrassPlot – Long Database Report 333

Introduction

The Palaearctic is the largest biogeographic realm of the world (Olson et al. 2001). It contains large areas of grass-lands (9.7 million km² or 22% of the Palaearctic realm), of both natural and secondary origin (Török & Dengler 2018). These grasslands harbour a high diversity of many taxonomic groups and encompass contrasting local di-versity. While some grassland types contain the majority of global vascular plant diversity records surveyed at small scales (Wilson et al. 2012), others can be very spe-cies poor (Dengler et al. 2016a). The high variation in lo-cal diversity and wide environmental gradients occupied (different biomes, elevational zones from the sea level to the alpine, diverse soil types, etc.) make Palaearctic grass-lands an ideal study object for understanding patterns and drivers of local plant diversity. Moreover, since many Palaearctic grasslands contain significant numbers of bryophytes and lichens, they allow testing of biodiver-sity patterns across taxa with contrasting biological traits (e.g. Löbel et al. 2006).

Plant community ecology is aimed at describing and understanding patterns of species composition and diver-sity recorded in small plots (“relevés” in phytosociology) in order to infer patterns and processes at local or re-gional scales. Macroecology, by contrast, analyses and explains patterns of diversity and its components across large regions, such as continents or the planet. The latter so far has typically relied on single species distribution data derived from sources such as the Global Biodiver-sity Information Facility (GBIF; https://www.gbif.org/) and gridded to coarse spatial grains, such as cells of 10,000 km² (Beck et al. 2012). This is far from the grain sizes at which relevant processes as the interaction among species and with their abiotic environment occur (Beck et al. 2012). In Europe, local studies on plant community composition, typically using the phytosociological method (Dengler et al. 2008; Guarino et al. 2018), surged in the last century (Schaminée et al. 2009). However, their grain sizes (e.g. Chytrý & Otýpková 2003) are still sig-nificantly larger than those at which some local processes, such as biotic interactions and edaphic filters (Siefert et al. 2012; Turtureanu et al. 2014), might act, which could be distances of centimetres or decimetres. Moreover, lo-cal studies have been criticized as being idiosyncratic and failing to derive general trends across regions (Chiarucci 2007; Dengler et al. 2011; Beck et al. 2012). A way to overcome this shortcoming, and to link community ecol-ogy to macroecology, is to unite individual vegetation-plot datasets into big databases that cover large geo-graphic areas (Dengler et al. 2011; Wiser 2016).

The European Vegetation Archive (EVA; Chytrý et al. 2016) and the global vegetation-plot database “sPlot” (Dengler & sPlot Core Team 2014), each with more than one million plots, are examples for recently assembled large vegetation-plot databases (Appendix 1). The first

pilot biodiversity studies of fine-grain plot data across large biogeographic extents (e.g. Wagner et al. 2017) de-monstrated the opportunities of large vegetation-plot da-tabases. However, analyses based on large databases face methodological difficulties. First, plot sizes can vary con-siderably among different schools, regions, decades and vegetation types (Chytrý & Otýpková 2003). In some phytosociological schools, plots might not even be de-limited in the field, have rather vague boundaries or ir-regular shapes to ensure so-called “floristic homogene-ity” (e.g. Géhu 2010). Second, the degree of completeness of the species list recorded within each plot can vary due to sampling effort or taxonomic skills. Moreover, in cer-tain phytosociological traditions, species or even whole life forms that were perceived as not belonging to an ”ideal” community were (and sometimes still are) not re-corded even when present in the plot (e.g. Géhu 1980).

While it is generally accepted that patterns and drivers of biodiversity are scale-dependent, this idea is based largely on theoretical considerations (Shmida & Wilson 1985) and insights from meta-analyses (Field et al. 2009; Siefert et al. 2012). By contrast, this hypothesis was rarely investigated in the field, using nested multi-scale data from the same location and plant community (e.g. Podani et al. 1993; Reed et al. 1993; Turtureanu et al. 2014). Moreover, notwithstanding that terrestrial vegetation is made up of taxa with contrasting biological traits, includ-ing vascular plants, bryophytes and lichens, large vegeta-tion databases to date have been focusing on vascular plants (see Appendix 1).

The outlined aspects inspired us to set up GrassPlot, the “Database of Scale-Dependent Phytodiversity Pat-terns in Palaearctic Grasslands”. The aim was to comple-ment EVA and sPlot with a specialised and selective data-base of multi-scale (and often multi-taxon) data from Palaearctic grasslands exhaustively sampled on precisely delimited plots. We use this Long Database Report to in-troduce GrassPlot to the scientific community, summa-rise its current content and demonstrate arising opportu-nities in the concert of existing databases.

History and governance of GrassPlot

The interest of some co-authors in small-scale species-area relationships (SARs) (Dengler 2009a; Wilson et al. 2012) motivated several regional studies in various dry grasslands in Europe (Dengler et al. 2004; Dengler & Boch 2008) and led then to the launch of the annual Re-search Expeditions (now: Field Workshops) of the Euro-pean Dry Grassland Group (EDGG; now: Eurasian Dry Grassland Group; Vrahnakis et al. 2013; http://www.edgg.org). The first expedition took place in 2009 in Transylvania, Romania. It revealed grasslands that scored several global records of small-scale vascular plant diver-sity (Wilson et al. 2012). With the aim of facilitating over-

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arching studies of SARs, Dengler et al. (2012) compiled available data in the “Database Species-Area Relation-ships in Palaearctic Grasslands” with 727 nested-plot se-ries comprising a total of 7,202 individual plot observa-tions. The EDGG Field Workshops continued to record standardised multi-scale vegetation data of grasslands across the Palaearctic, from Spain to Siberia (Vrahnakis et al. 2013). This effort resulted in several regional analyses of biodiversity patterns (e.g. Turtureanu et al. 2014; Pol-yakova et al. 2016). By 2016, the accumulation of data from the EDGG Field Workshops and from other re-searchers who had started to adopt the EDGG sampling methodology (Madari & Tănase 2016; Cancellieri et al. 2017) prompted the EDGG to create a comprehensive database. Initial steps included the compilation of an overview of existing datasets (Dengler et al. 2016a) and a description of the sampling approach (Dengler et al. 2016b), based on earlier suggestions by Dengler (2009b).

During an international workshop in Bayreuth in March 2017, the database was formally established with the name “GrassPlot” as a collaborative initiative within the EDGG (see http://bit.ly/2BIHmnq; logo in Fig. 1). The Data Property and Governance Rules (Bylaws) of GrassPlot (Supplement S1) have been set up to balance the interests of data providers and data users in a fair and transparent manner. In particular, data contributors re-main owners of their data, are informed about any plans to use their data and can opt-in as active co-authors of papers. Depending on the size and complexity, a dataset in GrassPlot can have one or several owners. The Grass-Plot Consortium is made up of these data owners and the 17 participants of the initial GrassPlot workshop. The Consortium elects the Governing Board every two years. The current Governing Board consists of J. Dengler (as Custodian), I. Biurrun (as Deputy Custodian) as well as T. Conradi, I. Dembicz, R. Guarino and A. Naqinezhad (as other members). It is responsible for managing Grass-Plot and for handling data requests as well as offering co-authorship under the Bylaws. Paper proposals can be submitted only by members of the GrassPlot Consor-tium or by author teams at least comprising one Consor-tium member.

GrassPlot is registered in the Global Index of Vegeta-tion-Plot Databases (GIVD; http://www.givd.info/; Dengler et al. 2011) under the ID EU-00-003 and has its own website with regularly updated information on the current content (http://bit.ly/2qKTQt2). Moreover, the Governing Board actively approached researchers world-wide whose publications were based on data that poten-tially met the GrassPlot criteria. This has maintained a constant inflow of datasets, accompanied by a substantial growth of the Consortium to currently 198 members from 35 countries.

Technical implementation

Since GrassPlot focuses on species richness and species-area relationships, its header data are stored in a single large spread sheet, with every row representing a (sub-) plot and containing information on species richness, the locality, vegetation structure and ecological parameters, plus an indication of nesting within larger plots. We adopted this solution because the nested nature of many plots is something that could not be easily accustomed in the common software for vegetation management (Tur-boveg 2; Hennekens & Schaminée 2001). Two additional spreadsheets list the metadata for the correspondent datasets and contact information of the Consortium members. As such, GrassPlot is organised differently from EVA and its contributing databases (Chytrý et al. 2016; see Appendix 1).

Compositional data, i.e. species composition and cover values, were not the original focus of GrassPlot and are not required parameters for new data (see Ap-pendix 1). However, since they were widely available for most individual datasets, they were also incorpo-rated. GrassPlot stores these data in long format .txt files. The latter were created semi-automatically based on the original, wide-format tables, provided by the data owners. Species names are taxonomically and no-menclaturally harmonized by a series of documented and repeatable R scripts (R Core Team 2017), similar to those used in sPlot (Purschke 2017). By this circum-stance we are not able to resolve identical names that refer to different taxonomic concepts (Jansen & Dengler 2010; see Appendix 1). This way, the data do not lend themselves for syntaxonomic analyses but they are a solid ground to analyse local diversity patterns and as-sembly rules.

The simple structure of the richness- and metadata in GrassPlot allows updates with little delay when new data are submitted. By contrast, compositional data are usu-ally integrated with a time lag as they can come in many different formats, and the harmonisation of their taxono-mies is challenging. GrassPlot data are stored in the .xlsx and .txt formats, which can be directly fed into different analytical software. While GrassPlot is updated continu-

Fig. 1. GrassPlot logo developed by Iwona Dembicz. It links the Stipa awns (reminiscent of the EDGG logo) to the multi-scale sampling approach of precisely delimited plots.

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sets (Supplements S2 and S3). In total, the database com-prises 168,997 plots of different grain sizes and 2,797 nested-plot series with at least four grain sizes (often con-sisting of several subseries). Most contributors have as-signed their plots to the “semi-restricted access” regime, few in “restricted access” and currently none in “free ac-cess” (Table 1). For the majority of plots (98%), the own-ers also provided compositional data although these are not fully integrated yet (Table 1).

Geographically, the plots range from Morocco in the west (9.2° W) to Japan in the east (161.6° E) and from Tibet (China) in the south (28.6° N) to Svalbard (Norway) in the north (77.9° N). The highest density of plots was recorded in temperate Europe (Fig. 2). In total, the plots originate from 36 countries, with Spain having the highest number (54,608 plots) and Austria the highest density (15.62 plots per 100 km²) of plots (Table 2). However, GrassPlot also contains relatively high densities of plots in countries that were hitherto only poorly represented in EVA (Chytrý et al. 2016) and sPlot (Dengler & sPlot Core Team 2014), namely Iran, Israel, Norway and Sweden. Plot elevation ranges from sea level (0 m a.s.l.) to 5,197 m a.s.l., with the largest fraction encompassing 2001–3000 m a.s.l. (Table 1). In total, data were sampled during the period of 1948 to 2017, with 79% of all plots surveyed in the decade of 2000–2009 (Table 1). Currently, 74% of all plots are syntaxo-nomically assigned to a class or a more precise level (Table 3). The temperate dry grasslands of the Festuco-Brometea (21%) and the Oromediterranean Festucetea indigestae (18%) are the best represented classes.

ously, each version is numbered and stored, enabling analyses with older versions.

Content of GrassPlot v. 1.00

GrassPlot collects vegetation-plot data of grasslands in the widest sense (i.e. everything except forests, tall shrub-lands, aquatic and segetal communities) from the Palae-arctic biogeographic realm (i.e. Europe, North Africa, West, Central, North and Northeast Asia). With respect to sampling methodology, GrassPlot is more restrictive than typical vegetation-plot databases. It only includes data of plots with one of our eight standard grain sizes: 0.0001, 0.001, 0.01, 0.1, 1, 10 100, 1,000 m². However, we also allow deviations up to 10% from these grain sizes, e.g. 9 m² instead of 10 m². Nested-plot series with at least four different grain sizes are also included; for the latter, any grain size is allowed. Plots must have been precisely delimited in the field (e.g. with a tape around the peri-meter or with frames for smaller sizes) and thoroughly been sampled at least for vascular plants, but preferen-tially also for terricolous bryophytes and lichens. Grass-Plot accepts (i) pure richness data (together with the required metadata) or (ii) complete vegetation plots (compositional data), i.e. species identities with presence-absence, cover, abundance or any other measure of domi-nance.

The first publicly released GrassPlot version 1.00 of 14 January 2018 contains data from 126 contributing data-

Table 1. Overview of some key parameters of GrassPlot v. 1.00 in terms of access regime, quality of the data, methodological aspects as well as temporal and elevational distribution. The column “NA” indicates the fraction of plots in GrassPlot for which the respective field is currently not filled.

Parameter NA Frequency distribution of parameter values

Availability of data

– Access regime – 1 – restricted access (1.7%); 2 – semi-restricted access (98.3%); 3 – free access (0.0%)

– Availability of compositional data – Yes (97.7%); to be provided later (0.2%); no (2.1%)

Methodological aspects

– Recording method <0.3% Shoot presence (87%); rooted presence (12.7%)

– Plot shape – Squares (75.3%); rectangles 1:2 (22.5%); rectangles 1:1.6 (0.5%); rectangles more elongated than 1:2 (< 0.1%); circles (1.6%)

– Accuracy of coordinates 0.4% ≤ 1 m (3.4%); 1.1–10 m (30.1%); 11–100 m (6.2%); 101–1,000 m (59.1%); > 1,000 m (0.7%)

Distribution of plots

– Year of recording – Before 1980 (< 0.1%); 1980–1989 (2.4%); 1990–1999 (2.7%); 2000–2009 (79.1%); 2010 and later (15.7%)

– Elevation 3.9% ≤ 10 m a.s.l. (8.4%); 11–100 m a.s.l. (17.2%); 101–1,000 m a.s.l. (12.1%); 1,001–2,000 m a.s.l. (12.0%); 2,001–3,000 m a.s.l. (34.2%); 3,001–4,000 m a.s.l. (16.0%); > 4,000 m a.s.l. (< 0.1%)

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Fig. 2. Maps showing the spatial distribution of the plots contained in GrassPlot v. 1.00. Grey dots refer to plots of any size, while black dots indicate nested-plot series with at least four different grain sizes.

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Table 3. The ten most represented phytosociological classes (according to Mucina et al. 2016) in GrassPlot 1.00, based on the numbers (N) and percentages of plots (%) in the total dataset.

Class Group N %

Festuco-Brometea Temperate dry grasslands 36,242 21.4%

Festucetea indigestae Alpine grasslands 31,086 18.4%

Juncetea trifidi Alpine grasslands 13,947 8.3%

Carici rupestris-Kobresietea bellardii Alpine grasslands 10,958 6.5%

Stipo-Trachynietea distachyae Mediterranean grasslands 6,697 4.0%

Molinio-Arrhenatheretea Temperate mesic and wet grasslands 6,206 3.7%

Koelerio-Corynephoretea canescentis Temperate dry grasslands 3,874 2.3%

Ammophiletea Coastal grasslands 3,550 2.1%

Juncetea maritimi Coastal grasslands 3,347 2.0%

Helichryso-Crucianelletea maritimae Coastal grasslands 3,259 1.9%

Other classes 7,283 4.3%

Not yet assigned to a class 42,548 25.2%

Table 2. Numbers (N) and densities of plots per country (or dependent territory), sorted by decreasing density of plots per 100 km². The twenty countries with the highest densities are given in the table. The remaining 16 countries can be found in the GIVD Fact Sheet. Area [km²] refers to the size of the respective territory.

Code Country Area [km²] N N/100 km²

AT Austria 83,855 13,099 15.62

ES Spain 504,790 54,608 10.82

IL Israel 20,724 1,795 8.66

SE Sweden 440,940 26,149 5.93

CH Switzerland 41,285 2,307 5.59

IT Italy 301,245 14,943 4.96

NO Norway 323,758 12,717 3.93

HU Hungary 93,030 3,648 3.92

EE Estonia 45,100 1,578 3.50

DE Germany 356,840 7,311 2.05

CZ Czech Republic 78,864 1,111 1.41

UK United Kingdom 244,587 2,886 1.18

PL Poland 312,685 2,778 0.89

NL Netherlands 41,160 354 0.86

SK Slovakia 49,035 405 0.83

IR Iran 1,648,000 12,992 0.79

RS Serbia 77,453 493 0.64

BG Bulgaria 110,910 572 0.52

SJ Svalbard and Jan Mayen 61,397 280 0.46

RO Romania 237,500 1,025 0.43

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The most frequent standard plot sizes are 0.01 m², fol-lowed by 1 m² and 9–10 m² (Table 4). Data for the com-plete terricolous vegetation (vascular plants, terricolous bryophytes and lichens) are available for 14,424 of all plots (8.5%) (Table 4, Fig. 2). Methodologically, the ma-jority of contributors used shoot sampling rather than rooted sampling (Table 1), which can make a big differ-ence for the assessment of vascular plant richness at small spatial grains (Dengler 2008; Güler et al. 2016; Cancellieri et al. 2017). Among plot shapes, squares were most fre-quently employed (75%), followed by rectangles with 1:2 edge length ratio (23%). Circles are the most compact shape, but difficult to delimit (see Güler et al. 2016), and were used in less than 2% of the records. The geographic coordinates stored in GrassPlot are nearly always more accurate than 1 km and in 3.4% of plots have an accuracy of 1 m or less (Table 1). Many structural (e.g. cover and height of vegetation layers; biomass) and ecological (e.g. topography, soil, land use) parameters are stored by GrassPlot in header data fields with harmonized termi-nology and units of measurement (see Supplement S4).

GrassPlot in the context of other large vegetation-plot databases

With EVA (Chytrý et al. 2016) and sPlot (Dengler & sPlot Core Team 2014) providing huge amounts of vege-tation-plot data of any vegetation type across Europe and the world (see Appendix 1), respectively, the need of an additional supra-national database like GrassPlot could

be questioned. Actually, EVA and sPlot are unprece-dented in spatial coverage (see Appendix 1). Being set up as all-purpose databases, however, they are not always suited optimally for certain specific questions. For this reason, specialised smaller databases have emerged e.g. with special focus on provision of plots with extensive and standardised soil data measured in the plot (e.g. Wamelink et al. 2012), for comparison of ecological im-pacts (e.g. PREDICTS, not only vegetation: Hudson et al. 2014) or for time-series in permanent plots (e.g. GLO-RIA: Pauli et al. 2012; forestREplot: Verheyen et al. 2017).

GrassPlot was set up with the aim to assemble data from Palaearctic grasslands by focusing on a multi-scale and multi-taxon approach. Multi-scale data are either not covered by the other large international vegetation-plot databases such as EVA (Chytrý et al. 2016) and sPlot (Dengler & sPlot Core Team 2014) or, if covered, not clearly labelled as such, reducing accessibility (see Ap-pendix 1). While one might think that alternatively one could just use the huge amount of plots of different sizes found in “normal” vegetation-plot databases, tests have shown that with this approach not even the most simple scaling law in ecology, the species-area relationship (SAR), is realistically depicted (see Chytrý 2001; Dengler et al. 2006). Therefore, GrassPlot complements the exist-ing databases by specifically filling the gap of multi-scale plot data. This enables analyses of scale-dependent pat-terns and processes across distant regions, which so far have been impossible. By contrast, EVA and sPlot are better suited for any type of analyses that requires high

Table 4. Number of plots (N) and the mean (Smean) and maximum (Smax) richness in GrassPlot (v. 1.00) across different plot sizes, and for vascular plants and the complete terricolous vegetation (vascular plants, bryophytes and lichens), respectively. Non-standard plot sizes include all other plot sizes (which are collected only in case of nested-plot series). Note that due to different samples, maxima of bigger plot sizes could sometimes be lower than for smaller plot sizes or that maxima for com-plete terricolous vegetation could sometimes be lower than for vascular plants only. Information on plot size pairs, such as 10 m² and 9 m², is combined in one line because based on species-area relationships with typical z-values between 0.15 and 0.30, the relative difference in richness would only be about 1.6–3.2%, i.e. negligible given the overall variability of the data.

Vascular plants Complete terricolous vegetation

Plot size N Smean Smax N Smean Smax

0.0001 m² 2,206 1.9 11 1,540 2.0 10

0.001 or 0.0009 m² 3,344 3.3 19 1,481 3.3 19

0.01 m² 66,011 3.8 24 2,524 6.5 29

0.1 or 0.09 m² 3,747 11.7 43 1,496 10.3 46

1 m² 17,216 13.8 79 2,008 18.2 82

10 or 9 m² 5,520 31.0 98 2,016 34.1 101

100 m² 2,565 31.9 127 824 46.8 134

1,000 or 900 or 1,024 m² 181 47.2 134 45 59.1 123

Non-standard plot sizes 68,207 2,490

Total 168,997 14,424

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spatial coverage (see Appendix 1). GrassPlot is not suited for purposes of vegetation classification due to the low spatial coverage/high spatial autocorrelation and the fact that plant names are only matched by synonymy but not by concepts (taxonyms) (see Appendix 1). Certain types of analyses could benefit from conducting them parallel in EVA/sPlot and in GrassPlot. For example, patterns of plot-scale species richness in European grasslands could be captured with high spatial resolution through the data contained in EVA, but the results might be considerably biased by regional differences in the sampling methodol-ogy (e.g. the completeness of species records). The same study done with GrassPlot would suffer much less from differences in sampling quality, but hardly could produce an alpha-richness map of Europe, simply because the available data are much sparser (see Fig. 2). A combina-tion of both data sources might thus allow taking advan-tage of both “approaches”.

While the majority of plots either are suited for EVA/sPlot or for GrassPlot, a rather small fraction is meeting the requirements of both (see Appendix 1): These are Pal-aearctic grassland plots on precisely delimited areas of 1, 9, 10 or 100 m² with thoroughly sampled species compo-sition, including “importance values” (i.e. cover, abun-dance, biomass, ...). It makes sense to include this limited amount of data in both EVA/sPlot and GrassPlot because they are stored in different formats that are readily pre-pared for different analyses. Good coordination between GrassPlot, EVA and sPlot is ensured because J. Dengler and I. Biurrun from the GrassPlot Governing Board are also involved in the EVA Coordinating Board and J. Dengler additionally in the sPlot Steering Committee. That way, redundant work is reduced and the effective inclusion of data whose qualities meet the criteria of sev-eral of these huge supranational databases in all of these is ensured (if data providers agree). Moreover, GrassPlot is also accepting small, local datasets that are in number of plots far below the thresholds of EVA/sPlot. Several such small datasets together could then be provided to EVA or sPlot.

Resumé and outlook

Despite being relatively small for an international vegeta-tion-plot database, we believe that GrassPlot can become a valuable tool in “community macroecology”. While the big databases EVA and sPlot are better suited for the ma-jority of purposes, GrassPlot can be advantageous for specific questions that require highly standardised data. Potential users are advised to select the most suitable da-tabase for a certain purpose based on the particular char-acteristics of these three (Appendix 1) and other data-bases.

Beyond that we hope that GrassPlot with its focus on methodological aspects of sampling and the prevalence

for a few “standard” plot sizes, will encourage many ve-getation scientists to consider these issues and thus pro-mote the collection of highly comparable data sets. Note-worthy, the same plot sizes (or a subset of these), each separated from the next by one order of magnitude, had previously been proposed in various frameworks (Shmida 1984; Peet et al. 1998; Chiarucci et al. 2001; Dengler 2009b).

GrassPlot is a dynamic database that will continue to integrate suitable datasets in the future. Researchers in possession of data that meet the GrassPlot specification and who wish to join our Consortium are welcome to contact our database manager (I. Biurrun). We particu-larly seek data from underrepresented regions (most of Asia, North Africa and some parts of Europe; see Fig. 2) and vegetation types (e.g. mesic, wet and Mediterranean grasslands; see Table 3) as well as generally plots with re-cording of bryophytes and lichens. Readers who wish to address a research idea with GrassPlot data are welcome to submit a project proposal jointly with a Consortium member of their choice to the Governing Board.

Author contributions

J.D. managed the predecessor databases of GrassPlot, while I.B. served as database manager from the start of GrassPlot onwards and V.W. handled the compositional data. J.D. led the writing of this report, with major contributions from V.W. as well as I.B., S.B., A.C., T.C., I.D., G.F., I.G.-M., R.G., M.J., A.N. and M.J.S. The figures were prepared by I.D. and the supplements by J.D., A.N. and I.G.-M. All oth-er authors contributed data to GrassPlot, checked and ap-proved the manuscript.

Acknowledgements

We thank the BayIntAn program of Bavarian Research Alli-ance (https://www.bayfor.org/en/research-funding/bayin-tan.php; grant no. UBT_2017_58) as well as the Bayreuth Centre of Ecology and Environmental Research (BayCEER; https://www.bayceer.uni-bayreuth.de/) for funding the GrassPlot workshop in Bayreuth. Furthermore, we are grateful to the International Association of Vegetation Sci-ence (IAVS; http://iavs.org/), the Eurasian Dry Grassland Group (EDGG; http://www.edgg.org/) and the Förderkreis für Allgemeine Naturkunde (Biologie) (FAN(B); http://www.fan-b.de/) for supporting the EDGG Expeditions/Field Workshops and all the colleagues who contributed to the high quality data in GrassPlot without being listed as co-authors. Two anonymous reviewers helped to improve the manuscript with their suggestions.

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Author addresses

Dengler, J. (Corresponding author, [email protected]; https://orcid.org/0000-0003-3221-660X)1,2,3, Wagner, V. ([email protected]; https://orcid.org/0000-0002-2665-9888)4, Dembicz, I. ([email protected]; https://orcid.org/0000-0002-6162-1519)5, Garcia-Mijangos, I. ([email protected]; https://orcid.org/0000-0002-1454-0433)6, Naqinezhad, A. ([email protected]; https://orcid.org/0000-0002-6642-7782)7, Boch, S. ([email protected]; https://orcid.org/0000-0002-4602-6279)8, Chiarucci, A. ([email protected]; https://orcid.org/0000-0003-2814-5343)9, Conradi, T. ([email protected]; https://orcid.org/0000-0003-2360-9284)2,10, Filibeck, G. ([email protected]; https://orcid.org/0000-0002-4187-9467)11, Guarino, R. ([email protected]; https://orcid.org/0000-0003-0106-9416)12, Janišová, M. ([email protected]; https://orcid.org/0000-0002-6445-0823)13, Steinbauer, M.J. ([email protected]; https://orcid.org/0000-0002-7142-9272)14, Aćić, S. ([email protected]; https://orcid.org/0000-0001-6553-3797)15, Acosta, A.T.R. ([email protected]; https://orcid.org/0000-0001-6572-3187)16, Akasaka, M. ([email protected]; https://orcid.org/0000-0002-0143-0841)17, Allers, M.-A. ([email protected])18, Apostolova, I. ([email protected])19, Axmanová, I. ([email protected])20, Bakan, B. ([email protected] )21, Baranova, A. ([email protected])22, Bardy-Durchhalter, M. ([email protected])23, Bartha, S. ([email protected])24, Baumann, E. ([email protected])25, Becker, T. ([email protected])26, Becker, U. ([email protected])27, Belonovskaya, E. ([email protected])28, Bengtsson, K. ([email protected])29, Benito Alonso, J.L. ([email protected]; https://orcid.org/0000-0003-1086-8834)30, Berastegi, A. ([email protected])31, Bergamini, A. ([email protected])8, Bonini, I. ([email protected])32, Bruun, H.H. ([email protected]; https://orcid.org/0000-0003-0674-2577)33, Budzhak, V. ([email protected])34, Bueno, A. ([email protected])35, Campos, J.A. ([email protected])6, Cancellieri, L. ([email protected]; https://orcid.org/0000-0002-0102-259X)11, Carboni, M. ([email protected])36, Chocarro, C. ([email protected]; https://orcid.org/0000-0003-0405-0737)37, Conti, L. ([email protected])16, Czarniecka-Wiera, M. ([email protected]; https://orcid.org/0000-0003-3294-5853)38, De Frenne, P. ([email protected])39, Deák, B. ([email protected]; https://orcid.org/0000-0001-6938-1997)40, Didukh, Y.P. ([email protected])41, Diekmann, M. ([email protected])42, Dolnik, C. ([email protected])43, Dupré, C. ([email protected])42, Ecker, K. ([email protected])8,

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Ermakov, N. ([email protected])44, Erschbamer, B. ([email protected]; https://orcid.org/0000-0002-6792-1395)45, Escudero, A. ([email protected])46, Etayo, J. ([email protected])47, Fajmonová, Z. ([email protected])48, Felde, V.A. ([email protected])49, Fernández Calzado, M.R. ([email protected])50, Finckh, M. ([email protected])51, Fotiadis, G. ([email protected])52, Fracchiolla, M. ([email protected])53, Ganeva, A. ([email protected])19, García-Magro, D. ([email protected])6, Gavilán, R.G. ([email protected])54, Germany, M. ([email protected])55, Giladi, I. ([email protected]; https://orcid.org/0000-0003-0240-7480)56, Gillet, F. ([email protected]; https://orcid.org/0000-0002-3334-1069)57, Giusso del Galdo, G.P. ([email protected])58, González, J.M. ([email protected]; https://orcid.org/0000-0001-7289-8321)46, Grytnes, J.-A. ([email protected]; https://orcid.org/0000-0002-6365-9676)49, Hájek, M. ([email protected])20, Hájková, P. ([email protected])20, Helm, A. ([email protected])59, Herrera, M. ([email protected])6, Hettenbergerová, E. ([email protected])20, Hobohm, C. ([email protected]; https://orcid.org/0000-0001-9067-9325)60, Hüllbusch, E.M. ([email protected])2, Ingerpuu, N. ([email protected])59, Jandt, U. ([email protected])61, Jeltsch, F. ([email protected]; https://orcid.org/0000-0002-4670-6469)62, Jensen, K. ([email protected])63, Jentsch, A. ([email protected])64, Jeschke, M. ([email protected])65, Jiménez-Alfaro, B. ([email protected]; https://orcid.org/0000-0001-6601-9597 )66, Kącki, Z. ([email protected])38, Kakinuma, K. ([email protected])67, Kapfer, J. ([email protected])68, Kavgacı, A. ([email protected]; https://orcid.org/0000-0002-4549-3668)69, Kelemen, A. ([email protected])40, Kiehl, K. ([email protected])70, Koyama, A. ([email protected])71, Koyanagi, T.F. ([email protected])72, Kozub, Ł. ([email protected])5, Kuzemko, A. ([email protected]; https://orcid.org/0000-0002-9425-2756)41, Kyrkjeeide, M.O. ([email protected])73, Landi, S. ([email protected]; https://orcid.org/0000-0002-6795-2132)74, Langer, N. ([email protected])75, Lastrucci, L. ([email protected]; https://orcid.org/0000-0003-4455-389X)53, Lazzaro, L. ([email protected] ; https://orcid.org/0000-0003-0514-0793)53, Lelli, C. ([email protected]; https://orcid.org/0000-0002-0497-828X)9, Lepš, J. ([email protected])76, Löbel, S. ([email protected])77, Luzuriaga, A.L. ([email protected])46, Maccherini, S. ([email protected])32, Magnes, M. ([email protected])78, Malicki, M. ([email protected])79, Marcenò, C. ([email protected]; https://orcid.org/0000-0003-4361-5200)6, Mardari, C. ([email protected])80, Mauchamp, L. ([email protected])57, May, F. ([email protected])81, Michelsen, O. ([email protected])82, Molero Mesa, J. ([email protected])50, Molnár, Z. ([email protected])24, Moysiyenko, I.Y. ([email protected])83, Nakaga, Y.K. ([email protected])84, Natcheva, R. ([email protected])19, Noroozi, J. ([email protected])85, Pakeman, R.J. ([email protected])86, Palpurina, S. ([email protected]; https://orcid.org/0000-0003-0416-5622)20, Pärtel, M. ([email protected]; https://orcid.org/0000-0002-5874-0138)59, Pätsch, R. ([email protected])87, Pauli, H. ([email protected]; https://orcid.org/0000-0002-9842-9934)23, Pedashenko, H. ([email protected])19, Peet, R.K. ([email protected]; https://orcid.org/0000-0003-2823-6587)88, Pielech, R. ([email protected]; https://orcid.org/0000-0001-8879-3305)89, Pipenbaher, N. ([email protected])21, Pirini, C. ([email protected])90, Plesková, Z. ([email protected])20, Polyakova, M.A. ([email protected])44, Prentice, H.C. ([email protected])91, Reinecke, J. ([email protected])92, Reitalu, T. ([email protected])93, Rodríguez-Rojo, M.P. ([email protected])94, Roleček, J. ([email protected])48,20, Ronkin, V. ([email protected])95, Rosati, L. ([email protected])96, Rosén, E. ([email protected])97, Ruprecht, E. ([email protected])98, Rusina, S. ([email protected])99, Sabovljević, M. ([email protected]; https://orcid.org/0000-0001-5809-0406)100, Sánchez, A.M. ([email protected]; https://orcid.org/0000-0002-6220-3001)46, Savchenko, G. ([email protected])95, Schuhmacher, O. ([email protected])101, Škornik, S. ([email protected])21, Sperandii, M.G. ([email protected]; https://orcid.org/0000-0002-2507-5928)16, Staniaszek-Kik, M. ([email protected])102, Stevanović-Dajić, Z. ([email protected]; https://orcid.org/0000-0001-9275-4499)15, Stock, M. ([email protected])103, Suchrow, S. ([email protected])63, Sutcliffe, L.M.E. ([email protected])104, Swacha, G. ([email protected])38, Sykes, M. ([email protected]; https://orcid.org/0000-0002-8392-9602)105, Szabó, A. ([email protected])106, Talebi, A. ([email protected])107, Tănase, C. ([email protected])108, Terzi, M. ([email protected])109, Tölgyesi, C. ([email protected])110, Torca, M. ([email protected])6, Török, P. ([email protected]; https://orcid.org/0000-0002-4428-3327)111, Tóthmérész, B. ([email protected]; https://orcid.org/0000-0002-4766-7668)111, Tsarevskaya, N. ([email protected])28, Tsiripidis, I. ([email protected]; https://orcid.org/0000-0001-9373-676X)90, Tzonev, R. ([email protected])112, Ushimaru, A. ([email protected]; https://orcid.org/0000-0002-2051-1233)84, Valkó, O. ([email protected]; https://orcid.org/0000-0001-7919-6293)40, van der Maarel, E. ([email protected])113, Vanneste, T. ([email protected])39, Vashenyak, I. ([email protected])114, Vassilev, K. ([email protected])19, Viciani, D. ([email protected]; https://orcid.org/0000-0003-3422-5999)53, Villar, L. ([email protected])115, Virtanen, R. ([email protected]; https://orcid.org/0000-0002-8295-8217)116, Vitasović Kosić, I. ([email protected] )117, Wang, Y. ([email protected])92, Weiser, F. ([email protected])25, Went, J. ([email protected])2, Wesche, K. ([email protected])92,3, White, H. ([email protected])118, Winkler, M. ([email protected]; https://orcid.org/0000-0002-8655-9555)23, Zaniewski, P.T. ([email protected])119, Zhang, H. ([email protected])120, Ziv, Y. ([email protected])121, Znamenskiy, S. ([email protected])122 & Biurrun, I. ([email protected]; https://orcid.org/0000-0003-1160-235X)6

1 Vegetation Ecology Group, Institute of Natural Resource Sciences (IUNR), Zurich University of Applied Sciences (ZHAW), Grüentalstr. 14, Postfach, 8820 Wädenswil, Switzerland

2 Plant Ecology, Bayreuth Center of Ecology and Environmental Research (BayCEER), University of Bayreuth, Universitäts-str. 30, 95447 Bayreuth, Germany

3 German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany4 Department of Biological Sciences, University of Alberta, Edmonton, AB, T6G 2R3, Canada5 Department of Plant Ecology and Environmental Conservation, Faculty of Biology, University of Warsaw, ul. Żwirki i Wigury

101, 02-089 Warsaw, Poland

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6 Department of Plant Biology and Ecology, University of the Basque Country UPV/EHU, P.O. Box 644, 48080 Bilbao, Spain7 Department of Biology, Faculty of Basic Sciences, University of Mazandaran, Babolsar, P.O. Box 47416-95447, Mazandaran,

Iran.8 Swiss Federal Research Institute WSL, Zürcherstrasse 111, 8903 Birmensdorf, Switzerland9 Department of Biological, Geological and Environmental Sciences, University of Bologna, via Irnerio 42, 40126 Bologna, Italy10 Ecoinformatics and Biodiversity, Department of Bioscience, Aarhus University, 8000 Aarhus, Denmark11 Department of Agricultural and Forestry Science (DAFNE), University of Tuscia, 01100 Viterbo, Italy12 Department STEBICEF – Botanical Unit, University of Palermo, via Archiarafi 38, 90123 Palermo, Italy13 Institute of Botany, Plant Science and Biodiversity Center, Slovak Academy of Sciences, Ďumbierska 1, 974 11 Banská Bystrica,

Slovakia14 GeoZentrum Nordbayern, Department of Geography and Geosciences, Friedrich-Alexander University Erlangen-Nürnberg

(FAU), Loewenichstr. 28, 91054 Erlangen, Germany15 Department of Botany, Faculty of Agriculture, University of Belgrade, Nemanjina 6, 11080 Belgrade-Zemun, Serbia16 Dipartimento di Scienze, Università degli Studi di Roma Tre, Viale Marconi, 446, 00146 Roma, Italy17 Institute of Agriculture, Tokyo University of Agriculture and Technology, Fuchu, 183-8509 Tokyo, Japan18 Albert-Einstein-Str. 11a, 14473 Potsdam, Germany19 Institute of Biodiversity and Ecosystem Research, Bulgarian Academy of Sciences, 23 Acad. Georgi Bonchev str., 1113 Sofia,

Bulgaria20 Department of Botany and Zoology, Masaryk University, Kotlárská 2, 61137 Brno, Czech Republic21 Biology Department, Faculty of Natural Sciences and Mathematics, University of Maribor, Koroska cesta 160, 2000 Maribor,

Slovenia22 CEN Center for Earth System Research and Sustainability, University of Hamburg, Bundesstr. 55, 20146 Hamburg, Germany23 GLORIA co-ordination, Institute for Interdisciplinary Mountain Research, Austrian Academy of Sciences, Silbergasse 30/3,

1190 Vienna, Austria24 Institute of Ecology and Botany, MTA Centre for Ecological Research, Alkotmány u. 2., 2163 Vácrátót, Hungary25 Biogeography, University of Bayreuth, Universitätsstr. 30, 95447 Bayreuth, Germany26 Geobotany, Regional and Environmental Science, University of Trier, Behringstr. 21, 54296 Trier, Germany27 Grüne Schule im Botanischen Garten, Johannes Gutenberg Universität Mainz, Anselm-Franz-von-Bentzel-Weg 9b,

55128 Mainz, Germany28 Institute of Geography, Russian Academy of Sciences, Staromonetny per., 29, 119017 Moscow, Russia29 Department of Ecology and Genetics (EBC), Uppsala University, Campus Gotland, 62167 Visby, Sweden30 JOLUBE Consultor Botánico, C/ Mariano Rguez. De Ledesma 4-3ºA, 22700 Jaca, Spain31 Biodiversity Team, Gestión Ambiental de Navarra, S.A., Padre Adoain 219, Bajo, 31015 Pamplona, Spain32 Life Sciences, University of Siena, P.A. Mattioli, 4, 53100 Siena, Italy33 Section for Ecology and Evolution, Department of Biology, University of Copenhagen, Universitetsparken 15,

2100 Copenhagen, Denmark34 Botany and Natural Protection Department, Chernivtsi National University, Fedkovich Street 11, 58022 Chernivtsy, Ukraine35 Instituto de Recursos Naturales y Ordenación del Territorio (INDUROT), Universidad de Oviedo, Campus de Mieres.

Edificio de Investigación, 33600 Mieres, Spain36 Laboratoire d’Ecologie Alpine, UMR-CNRS. 5553, Université Grenoble Alpes, BP 53, 38041 Grenoble Cedex 9, France37 Crop and Forest Science, Universitat de Lleida, Rovira Roure 177, 25110 Lleida, Spain38 Botanical Garden, University of Wrocław, Sienkiewicza 23, 50-335 Wroclaw, Poland39 Forest & Nature Lab, Ghent University, Geraardsbergsesteenweg 267, 9090 Gontrode, Belgium40 Department of Ecology, University of Debrecen, Egyetem tér 1, 4032 Debrecen, Hungary41 Geobotany and Ecology Department, M.G. Kholodny Institute of Botany NAS of Ukraine, Tereschenkivska str. 2, 1601 Kyiv,

Ukraine42 Vegetationsökologie und Naturschutzbiologie, FB 2, Universität Bremen, Leobener Str. 5, 28359 Bremen, Germany43 Ecology Centre Kiel, Kiel University, Olshausenstr. 40, 24098 Kiel, Germany44 Central Siberian Botanical Garden, Russian Academy of Science, Zolotodolinskaya 101, 630090 Novosibirsk, Russia45 Department of Botany, University of Innsbruck, Sternwartestr. 15, 6020 Innsbruck, Austria46 Department of Biology, Geology, Physics and Inorganic Chemistry, Universidad Rey Juan Carlos, Tulipán s/n, 28933 Móstoles,

Spain47 Navarro Villoslada 16-3ºdcha, 31003 Pamplona, Spain48 Department of Vegetation Ecology, Institute of Botany, Czech Academy of Sciences, Lidická 25/27, 60200 Brno, Czech

Republic49 Department of Biology, University of Bergen, Postbox 7803, 5020 Bergen, Norway50 Department of Botany, Faculty of Pharmacy, University of Granada, Campus de Cartuja s/n, 18071 Granada, Spain51 Biodiversity, Evolution and Ecology of Plants (BEE), Biocentre Klein Flottbek and Botanical Garden, University of Hamburg,

Ohnhorststr. 18, 22609 Hamburg, Germany52 Department of Forestry and Natural Environment Management, TEI (Technological Education Institute) of Sterea Ellada,

Dimokratias 3, 36100 Karpenisi, Greece53 Department of Agricultural and Environmental Science, University of Bari, Via Orabona 4, 70126 Bari, Italy54 Departamento de Biología Vegetal II, Facultad de Farmacia, Universidad Complutense de Madrid, 28040 Madrid, Spain55 Institute for Ecosystem Research, Kiel University, Olshausenstr. 75, 24118 Kiel, Germany

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56 Mitrani Department of Desert Ecology, Swiss Institute for Dryland Environmental and Energy Research, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boqer Campus, 84990 Midreshet Ben Gurion, Israel

57 UMR Chrono-environnement, Université de Franche-Comté, 16 route de Gray, 25030 Besançon, France58 Department of Biological, Geological and Environmental Sciences (BiGeA), University of Catania, via A. Longo 19,

95125 Catania, Italy59 Institute of Ecology and Earth Sciences, University of Tartu, Lai 40, 51005 Tartu, Estonia60 Interdisciplinary Institute of Environmental, Social and Human Sciences, University of Flensburg, Auf dem Campus 1,

24943 Flensburg, Germany61 Geobotany and Botanical Garden, Institute of Biology, Martin Luther University, Am Kirchtor 1, 6108 Halle (Saale), Germany62 Plant Ecology and Nature Conservation, University of Potsdam, Am Mühlenberg 3, 14476 Potsdam, Germany63 Applied Plant Ecology, Biocentre Klein Flottbek, University of Hamburg, Ohnhorststr. 18, 22609 Hamburg, Germany64 Disturbance Ecology, Bayreuth Center of Ecology and Environmental Research (BayCEER), University of Bayreuth,

Universitätsstr. 30, 95447 Bayreuth, Germany65 Friesenstr. 47, 82223 Eichenau, Germany66 Research Unit of Biodiversity (CSIC, UO, PA), Oviedo University, Campus de Mieres, 33600 Mieres, Spain67 NASA Goddard Institute for Space Studies, Columbia University, 2880 Broadway, NY 10025 New York, United States68 Division for Geography and Statistics, Department of Landscape Monitoring, Norwegian Institute of Bioeconomy Research,

Holtveien 66, 9269 Tromsø, Norway69 Southwest Anatolia Forest Research Institute, POB 264, 07002 Antalya, Turkey70 Vegetation Ecology and Botany, Faculty of Agricultural Sciences and Landscape Architecture, University of Applied Sciences

Osnabrück, Oldenburger Landstr. 24, 49090 Osnabrück, Germany71 Institute for Sustainable Agro-ecosystem Services, University of Tokyo, 1-1-1, Midori-cho, Nishi-Tokyo, 188-0002 Tokyo,

Japan72 Field Studies Institute for Environmental Education, Tokyo Gakugei University, 4-1-1 Koganei, 184-8501 Tokyo, Japan73 Norwegian Institute for Nature Research, 7485 Trondheim, Norway74 Department of Sciences of Nature and Territory, University of Sassari, Via Piandanna, 07100 Sassari, Italy75 Zum Schwärzesee 27, 16227 Eberswalde, Germany76 Department of Botany, Faculty of Science, University of South Bohemia, Branišovská 31, 370 05 Ceske Budejovice,

Czech Republic77 Landscape Ecology and Environmental Systems Analysis, Institute of Geoecology, Langer Kamp 19c, 38106 Braunschweig,

Germany78 Institute of Plant Sciences, University of Graz, Holteigasse 6, 8010 Graz, Austria79 Department of Botany, University of Wrocław, ul. Kanonia 6/8, 50-328 Wrocław, Poland80 Anastasie Fatu Botanical Garden, Alexandru Ioan Cuza University from Iași, Dumbrava Roșie 7-9, 700487 Iași, Romania81 Biodiversity Synthesis Research Group, German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig,

Deutscher Platz 5e, 04103 Leipzig, Germany82 NTNU Sustainability, Norwegian University of Science and Technology, 7491 Trondheim, Norway83 Department of Botany, Kherson State University, ul. Universytetska 27, 73000 Kherson, Ukraine84 Graduate School of Human Development and Environment, Kobe University, 3-11 Tsrurukabuto, 657-8501 Kobe, Japan85 Department of Botany and Biodiversity Research, University of Vienna, Rennweg 14, 1030 Vienna, Austria86 Ecological Sciences, The James Hutton Institute, Craigiebuckler, AB15 8QH Aberdeen, United Kingdom87 Vegetation and Phytodiversity Analysis, Georg-August University of Göttingen, Untere Karspüle 2, 37073 Göttingen,

Germany88 Department of Biology, University of North Carolina, 27599-3280 Chapel Hill, NC, United States89 Department of Forest Biodiversity, Faculty of Forestry, University of Agriculture in Kraków, al. 29 Listopada 46,

31-425 Kraków, Poland90 Department of Botany, School of Biology, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece91 Department of Biology, Lund University, Ecology Building, 22362 Lund, Sweden92 Botany Department, Senckenberg Museum of Natural History Görlitz, Am Museum 1, 2826 Görlitz, Germany93 Institute of Geology, Tallinn University of Technology, Ehitajate tee 5, 19086 Tallinn, Estonia94 Institute of Environmental Sciences, University of Castilla-La Mancha, Avenida Carlos III, s/n, 45071 Toledo, Spain95 Dept of Zoology and Ecology, Kharkiv National University, 4 Svobody Sq, 61022 Kharkiv, Ukraine96 School of Agricultural, Forest, Food and Environmental Sciences, University of Basilicata, Via Ateneo Lucano 10,

85100 Potenza, Italy97 Department of Ecology and Genetics (EBC), Uppsala University, Norbyvägen 18 D, 75236 Uppsala, Sweden98 Hungarian Department of Biology and Ecology, Babes-Bolyai University, Republici str. 42, 400015 Cluj-Napoca, Romania99 Faculty of Geography and Earth Sciences, University of Latvia, 1 Jelgavas Street, 1004 Riga, Latvia100 Institute for Botany and Botanical Garden “Jevremovac”, Faculty of Biology, University of Belgrade, Takovska 43,

11000 Belgrade, Serbia101 NABU Hamburg, Am Stadtbad 45, 29451 Dannenberg, Germany102 Department of Geobotany and Plant Ecology, University of Łódź, Banacha 12/16, 90- 237 Łódź, Poland103 Schleswig-Holstein Agency for Coastal Defence, National Park and Marine Conservation, National Park Authority,

Schlossgarten 1, 25832 Tönning, Germany104 Plant Ecology and Ecosystem Research, Georg-August University of Göttingen, Untere Karspüle 2, 37073 Göttingen,

Germany

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105 Department of Physical Geography and Ecosystem Science, Lund University, Sölvegatan 12, 22362 Lund, Sweden106 Romanian Ornithological Society, Gh. Dima street 49, 400342 Cluj-Napoca, Romania107 School of Biology and Center of Excellence in Phylogeny of Living Organisms, College of Science, University of Tehran,

Enghelab, 14155-6455 Tehran, Iran108 Faculty of Biology, Alexandru Ioan Cuza University, Carol I 20A, 700505 Iași, Romania109 Institute of Biosciences and Bioresources (IBBR), Italian National Council of Research (CNR), Via Amendola 165/A,

70126 Bari, Italy110 Department of Ecology, University of Szeged, Közép fasor 52, 6726 Szeged, Hungary111 MTA-DE Lendület Functional and Restoration Ecology Research Group, Egyetem tér 1, 4032 Debrecen, Hungary112 Department of Ecology and Environmental Protection, Sofia University ‘St. Kliment Ohridski’, 8 Dragan Tzankov Blvd.,

1164 Sofia, Bulgaria113 Centre for Ecological and Evolutionary Studies, University of Groningen, Linnaeusborg - Nijenborgh 7 Building U,

9747 AG Groningen, Netherlands114 Khmelnytskyi Institute of Interregional Academy of Personnel Management, Prospect Myru Str., 101A, 29015 Khmelnytskyi,

Ukraine115 Instituto Pirenaico de Ecología, Consejo Superior de Investigaciones Científicas, 22700 Jaca, Spain116 Ecology & Genetics, University of Oulu, P.O Box 3000, 90014 Oulu, Finland117 Faculty of Agriculture, Department of Agricultural Botany, University of Zagreb, Svetošimunska cesta 25, 10000 Zagreb,

Croatia118 School of Biology and Environmental Science, Earth Institute, University College Dublin, Belfield, Dublin 4, Ireland119 Department of Forest Botany, Faculty of Forestry, Warsaw University of Life Sciences – SGGW, Nowoursynowska 159,

02-776 Warsaw, Poland120 Key Laboratory of Vegetation Restoration and Management of Degraded Ecosystems, South China Botanical Garden,

Chinese Academy of Sciences, Xingke Road 723, 510650 Guangzhou, China121 Department of Life Sciences, Ben-Gurion University of the Negev, 84105 Negev, Israel122 Institute of Biology, Karelian Research Centre RAS, Pushkinskaya 11, 185910 Petrozavodsk, Russia

Electronic Supplement

Supplementary material associated with this article is embedded in the pdf of this article. The online version of Phytocoenologia is hosted at the journal’s website http://www.schweizerbart.com/journals/phyto. The publisher does not bear any liability for the lack of usability or correctness of supplementary material.

Supplement S1: GrassPlot Bylaws.Supplement S2: Overview of the datasets in GrassPlot 1.00.Supplement S3: Bibliographic references to the datasets contained in GrassPlot 1.00.Supplement S4: Overview of the content of the header data fields other than those in Tables 1–4.

Please save the electronic supplement contained in this pdf-file by clicking the blue frame above. After saving rename the file extension to .zip (for security reasons Adobe does not allow to embed .exe, .zip, .rar etc. files).

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Appendix 1. Comparison of the three large supra-national databases of vegetation-plot data: EVA, sPlot and GrassPlot, in-dicating their similarities and differences (information as of 14 January 2018).

Aspect EVA sPlot GrassPlot

Scope

Geographic scope Europe (+ Canary Islands, Turkey, Caucasus countries)

World Palaearctic biogeographic realm

Vegetation types included All All Grasslands and other open habitats

Plot sizes Any in the range 1–1,000 m² and also plots without reported size

Any in the range 1–10,000 m²

Eight standard grain sizes from 0.0001 to 1,000 m² (other sizes only if part of nested plot series)

Nested plots Not supported Not supported Specialised in nested plots; information on hierarchy of nesting is stored

Delimitation of plots and comprehensiveness of sampling

No requirements No requirements; even plots are included where only dominant species have been sampled (but this information is available)

Only plots that have been precisely delimited in the field and sampled compre-hensively

Data types and formats

Information contained in the database

Plots with compositional data Plots with compositional data

Plots with compositional data or just richness data + metadata

Format in which the data are stored and provided

Turboveg 2 databases combined in a Turboveg 3 database

Turboveg 2 databases combined in a Turboveg 3 database; data provision as R Data.table with harmo-nized information

Spread sheet for richness, methodological and environmental data; long table format in R for compositional data

Matching with plant trait and phylogenetic data available

No (but in the future possible via collaboration with sPlot/TRY)

Yes No

Available information per plot

Recording of non-vascular plants

Rare and if available often not comprehensive; plots with comprehensive data cannot be extracted

Rare and if available often not comprehensive; plots with comprehensive data cannot be extracted

Often included and then comprehensive

Importance values of species

Normally required (Br.-Bl., % or similar)

Multitude of quantitative scales, but also presence-absence

Importance values (often %) or just presence-absence

Precision of plot coordinates

High to very low; field often not filled

High to very low Mostly high

Environmental data measured in the plot

Not standardised Not standardised Standardised and thus directly usable

Names of plants provided Standardised to an internal taxonomic backbone for Europe (SynBioSys Taxon Database), also taking into account different meanings of the same name in different floras

Harmonized with online tools, taking into account synonymy, but not different meanings of the same name in different floras

Harmonized with online tools, taking into account synonymy, but not different meanings of the same name in different floras

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Aspect EVA sPlot GrassPlot

Current content

Plot number 1,474,590 1,121,244 168,997

Countries covered 57 160 36

Spatial density of available plots

High High in Europe, medium in parts of North America and Australia, sparse elsewhere

Relatively sparse

Overlap with the other databases in the table

The majority of EVA plots are also in sPlot

sPlot accepts European plots only via EVA

Overlap with EVA and sPlot is small and documented; it is recommended that plots that are suitable for EVA/sPlot and GrassPlot should be contributed twice

Responsible working groups and their rules

Affiliated with European Vegetation Survey (EVS)

German Centre for Integra-tive Biodiversity Research (iDiv)

Eurasian Dry Grassland Group (EDGG)

Website http://euroveg.org/eva-data-base

https://www.idiv.de/splot http://bit.ly/2qKTQt2

Governed by 7-head Coordinating Board 5-head Steering Committee 7-head Governing Board

Members 72 supranational, national and regional databases

110 supranational, national and regional databases, 2 continental data aggrega-tors

192 owners of 126 regional datasets

Required offers of opt-in authorships for analytical papers

No requirement, usually one co-author for each database that contributed at least (5%) 10% of the final dataset

One opt-in co-author for each database used in the study

One opt-in co-author for each dataset that contri-buted at least 2% of the final dataset

Appendix 1. cont.

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GrassPlot Bylaws, page 1 of 6

Supporting Information to the paper Dengler, J. et al. 2018. GrassPlot – a database of multi-scale plant diversity in Palaearctic grasslands. Phytocoenologia.

Supplement S1. GrassPlot Bylaws. Database of Scale-Dependent Phytodiversity Patterns in Palaearctic Grasslands (GrassPlot) (GIVD code: EU-00-003) Data Property and Governance Rules (Bylaws) Discussed and approved by the founding GrassPlot Consortium members, namely Idoia Biurrun, Steffen Boch, Alessandro Chiarucci, Timo Conradi, Francesco de Bello, Iwona Dembicz, Jürgen Dengler, Goffredo Filibeck, Itziar García-Mijangos, Riccardo Guarino, Monika Janišová, Swantje Löbel, Alireza Naqinezhad, Santiago Soliveres, Manuel J. Steinbauer, David Storch, Werner Ulrich and Viktoria Wagner, on 19 March 2017.

1. Status and purpose of the GrassPlot Database

The Database of Scale-Dependent Phytodiversity Patterns in Palaearctic Grasslands (short: GrassPlot) is a collaborative initiative within the framework of the Eurasian Dry Grassland Group (EDGG) of the International Association for Vegetation Science (IAVS). The purpose of GrassPlot is to establish and maintain a common data repository of high-quality vegetation-plot observations (i.e. records of plant taxon co-occurrence at particular sites) of grasslands and related vegetation types from the whole Palaearctic biogeographic realm, and to facilitate the use of these data for non-commercial purposes, mainly academic research and applications in nature conservation and ecological restoration. GrassPlot focuses specifically on multi-scale and multi-taxon sampling in precisely delimited plots with extensive environmental data. Obligatory requirements for data contributions to GrassPlot are: (a) origin in the Palaearctic biogeographic realm; (b) grassland vegetation in the wide sense, i.e. terrestrial and semi-terrestrial vegetation types dominated by hemicryptophytes, therophytes, geophytes, and occasionally bryophytes, lichens and chamaephytes (forests, shrublands, aquatic, ruderal and segetal vegetation are not considered); (c) careful sampling of precisely delimited plots with the aim of complete species lists, (d) providing details of sampling methodology (in particular, whether rooted or shoot presence was recorded and which plot shape was used), and (e) meeting one of the following criteria (or a combination of these): (i) data for one or several of the eight standard grain sizes (0.0001; 0.001 or 0.0009; 0.01; 0.1 or 0.09; 1; 10 or 9; 100; 1000 or 900 or 1024 m²) or (ii) nested-plot series with at least four different grain sizes. Plots meeting the following additional criteria are particularly welcome: (f) precise GPS coordinates; (g) complete sampling of one or several macroscopic non-vascular taxa of the terricolous vegetetation (bryophytes, lichens, “algae”) in addition to vascular plants; (h) multi-scale sampling; (i) direct cover estimates of species in percent for at least one grain size; and (j) environmental variables measured or determined at the plot scale (vegetation structure, topography, soil, land use).


2. Data contributions to the GrassPlot Database

Persons who are willing to contribute their own published or unpublished plot records or plot records of other authors which they digitised from the literature can apply to become a member of the GrassPlot Consortium. Applications should be sent to the Executive Committee of GrassPlot. If the offered data are not present in the database yet and meet the technical requirements of sampling and data storage, such offers will normally be accepted. Generally, data must be provided in an electronic format, but exceptionally unpublished data in paper format will be accepted if they fill important gaps. The complete data of the EDGG Field Workshops (formerly: EDGG Research Expeditions) automatically become part of the GrassPlot Database once they are prepared. In GrassPlot, the data of a specific project (e.g. a dissertation or an EDGG Field Workshop) are treated as an individual dataset. GrassPlot does not support the artificial splitting of coherent datasets into several independent datasets. A dataset in GrassPlot has a Dataset Custodian and one or several data owners (one of them being the Dataset Custodian), whose maximum number is defined by the GrassPlot Score, and who through data contribution become part of the GrassPlot Consortium (see Article 3). As dataset owners can be listed those persons who carried out the field work, determined the plants, analysed the soils or entered and harmonized the data. It is the responsibility of the Dataset Custodian to forward communication received from the GrassPlot Governing Board to the individual data owners and to inform the Executive Committee about any changes of their contact data. The GrassPlot Score rates the overall size and value of datasets in the GrassPlot context: First the numbers of all plots in a contributed dataset are summed up, using the following weights: 2 for plots ≥ 100 m², 1 for plots ≥ 1 m²; 0.5 for plots ≥ 0.01 m² and 0.25 for plots < 0.01 m². To this value, five times the number of nested-plot series (spanning at least three orders of magnitude) and the square root of the diameter of the study area in km (distance of the two plots furthest away from each other) are added. To derive the GrassPlot Score, the resulting raw score is multiplied by 2 if compositional data are also provided or if in addition to vascular plants also bryophytes and lichen have been sampled, or multiplied by 4 if both criteria are fulfilled. The fourth root of the GrassPlot Score rounded to the next integer defines the maximum number of data owners that can be assigned to one dataset. The individual GrassPlot Score of a data owner is the summation of the fractional GrassPlot Scores of all datasets of which this person is co-owner of. For the fractional scores each owner of a dataset is counted equal, except the Dataset Custodian who is counted twice. The individual GrassPlot Scores of data owners are used to weight votes in elections of the GrassPlot Governing Board (see Article 3). Having contributed plot records to GrassPlot does not limit other usages of these data by their owners. Unpublished data, i.e. data that do not exist in form of individual plot records in any publically accessible print or online sources, can be withdrawn from the GrassPlot Database by their Dataset Custodians at any time. Such withdrawal does not affect usages and publications that have already been approved at that point of time.

3. GrassPlot Consortium

The founding Consortium of the Database of Scale-Dependent Phytodiversity Patterns in Palaearctic Grasslands (short: GrassPlot Consortium) consists of the 17 researchers who participated physically or remotely in the initial GrassPlot Workshop, 6−10 March 2017, in Bayreuth, Germany. Individuals


or groups of individuals who agree with these Bylaws and contribute data to GrassPlot that meet the specifications defined by the GrassPlot Governing Board become members of the Consortium once their data are included in the common database, whereby the maximum number of data owners and thus Consortium members per dataset is defined by the GrassPlot Score (see above). Further, the GrassPlot Governing Board can appoint researchers without plot data provision as Consortium members who substantially contributed to the development of the GrassPlot initiative, be it via managing the database, preparing ancillary datasets or leading GrassPlot publications. The GrassPlot Consortium has the following functions: It (i) elects the GrassPlot Governing Board, and (ii) approves and potentially modifies these Bylaws. Moreover, the whole GrassPlot Consortium has to be informed about any intended usage of data retrieved from the GrassPlot directly or indirectly. Only members of the GrassPlot Consortium are entitled to propose paper projects using GrassPlot data, but non-members can submit a proposal jointly with a GrassPlot Consortium member. Communication within the GrassPlot Consortium is via e-mail; it thus is the responsibility of each GrassPlot Consortium member to provide the Executive Committee with a functioning e-mail address. Decisions by the GrassPlot Consortium (including elections) are made via electronic ballot (i.e. e-mail) within a 14-day period by simple majority among the returned votes. In these ballots, each GrassPlot Consortium member has one, two or three votes, based on the amount of data they contributed to the database. With an individual GrassPlot Score above 10,000 one has three votes, with an individual GrassPlot Score above 2,000 two votes and else one vote. Those who have been appointed as Consortium member without contribution of plot data always have one vote.

4. Governing Board, Executive Committee, Custodian and Deputy Custodian

GrassPlot is represented and governed by its Governing Board. The Governing Board is elected by the GrassPlot Consortium for two-year renewable terms. Eligible are all GrassPlot Consortium members who have been nominated by a GrassPlot Consortium member (including themselves) during a nomination period of at least 14 days. In the election each member can vote for up to seven candidates, with the votes being weighted with the factors indicated in Article 3. The seven candidates with the highest number of votes are elected; in case of a tie for the seventh position, all candidates with the same number of votes are also elected. If there are as many or fewer nominees than positions to be filled, the candidates are appointed without election. If more than half of the regular positions of the Governing Board have become vacant, they are re-filled by an election for the remaining time of the current term. The Governing Board is responsible for managing the GrassPlot Database and any ancillary data, processing incoming data requests as described in Article 6, representing GrassPlot on conferences and similar occasions, attracting additional data or paper projects, preparing grant proposals as well as for maintaining a GrassPlot website and updating the GrassPlot data on the GIVD website. The Governing Board can appoint additional persons from inside and outside the GrassPlot Consortium to assist them with the data management. The Governing Board elects from its members the GrassPlot Custodian and Deputy Custodian, who together form the GrassPlot Executive Committee. The Executive Committee is responsible for running the everyday business of GrassPlot and serving as main contact for requests from inside and outside the Consortium. Unlike the procedure described above, the first Governing Board for the period March 2017 – March 2019 will be assembled without prior election in order to facilitate a quick set-up of the first database version. This Governing Board will consist of Idoia Biurrun, Timo Conradi, Iwona Dembicz,


Jürgen Dengler, Riccardo Guarino, Alireza Naqinezhad and Viktoria Wagner, with Jürgen Dengler being Custodian and Idoia Biurrun Deputy Custodian, and was approved by the founding GrassPlot Consortium members.

5. Data availability regimes

At the time of data submission or update, the Dataset Custodian assigns one of the following data availability regimes to the dataset contributed: Regime 1: Restricted-access data require an explicit consent from the Dataset Custodian each time their use is requested. If the Dataset Custodian does not give such an approval (possibly after negotiation of details) within 14 days of distributing the paper proposal, the data will not be made available for the particular project. Regime 2: Semi-restricted-access data are generally available for all projects that are in agreement with these Bylaws. The Dataset Custodian will be informed by the GrassPlot Executive Committee at least 14 days before the release of data. If no objection is raised during this period, this will be considered as implicit permission to use these data for the particular purpose. Regime 3: Free-access data (typically those digitised from published sources) are available to a wider community of users. Their release does not require explicit or implicit consent of their originator for release. Contributors are encouraged to assign Regime 2 to their data. Data of EDGG Research Expeditions, EDGG Field Workshops or other official EDGG activities automatically are assigned to Regime 2, but without the possibility to deny the use of the data on a case-by-case basis.

6. Data requests and terms of data use

Individuals or groups of individuals who would like to use the GrassPlot data for research or application purposes must submit a proposal to the Custodian describing the aims, basic methods and an approximate delineation of the data set they require. Only proposals by GrassPlot Consortium members will be considered, but non-members can submit a proposal jointly with a GrassPlot Consortium member. Only supra-regional proposals that are based on multiple datasets are considered; regional projects should direct their requests directly to the respective Dataset Custodian(s). The proposal should contain (1) project title, (2) names and addresses of the applicant(s), indicating who of them is a Consortium member, (3) brief description of aims and methods of the study, (4) specification of the data needed (geographic area, plot sizes, compositional data, cryptogam treatment), (5) estimated time of delivery of results, e.g. manuscript submission, (6) envisaged publication(s), and (7) explicit statement that the applicant agrees with these Bylaws. For incoming proposals, the Governing Board will check (1) whether the applicant is eligible to obtain data according to these Bylaws, and if so, (2) whether these Bylaws are respected, whether (3) there is a reasonable link between the aims, expected outputs and data requested, and (4) there are any conflicts with already approved projects. If these initial assessments have been positive, the Executive Committee will send the request to all Dataset Custodians and all data owners whose plots are concerned.


Within 14 days after such a request has been sent out, contributors of Regime-1 data can agree to provide their data and those of Regime-2 data disagree with their usage in this particular case. After the deadline and based on the received answers of the Dataset Custodians, the Executive Committee will release the available data to the authors of the proposal. The Governing Board will make efforts to deliver the data as fast as possible; however, the speed of this service may be affected by the available labour force and funding. Upon data delivery the applicants must provide the result (e.g. a manuscript ready for submission) to the Executive Committee within two years. After this time they lose their mandate to use the data unless they ask for extension and get it granted. The Executive Committee is entitled to ask for the progress of projects using GrassPlot data at any point of time. Each publication and conference contribution using GrassPlot data must be sent to the Executive Committee before submission for a check whether the data were used in accordance with these Bylaws. This check will not deal with the scientific quality of the manuscript and the Executive Committee will keep any information contained in the manuscript as confidential until the paper is published. The applicants are not allowed to pass any data obtained from GrassPlot (including those under Regime 3) to third parties or use them for other purposes than stated in the original proposal. Data obtained from GrassPlot can be used for non-commercial purposes only and the final product (publication, report, software application) should contain a proper citation of the GrassPlot Database and of the original data sources (or at least main sources if there are many sources, possibly in an electronic appendix). Recommended references will be provided by the Executive Committee. The titles of projects that have received data from GrassPlot, together with the names of applicants, can be published on the GrassPlot website. The applicants will send the papers based on GrassPlot data to the Custodian upon their publication; links to these papers can be added to the GrassPlot website. The applicants are encouraged to report any errors found in the data to the custodians of the original databases.

7. Authorship

For report-style papers about the philosophy, content and functionality of the GrassPlot, including, but not restricted to, Long and Short Database Reports in Phytocoenologia or another journal as well as for the first descriptive paper on alpha diversity patterns (“data papers”, “opt-out papers”), all data owners who have contributed their data by the deadline specified by the lead will be offered co-authorship, regardless the extent of their contribution (in terms of data or paper writing). Authorship of other papers or reports completely or partly based on data received from GrassPlot (“research papers”, “opt-in papers”) requires that, in addition to data provision, one makes an intellectual contribution to at least one of the following aspects: (i) design of the study; (ii) statistical analyses; (iii) interpretation of the results; (iv) writing up or (v) revising the text. Potential opt-in authors (those authors who have not already been listed by the applicant in the application), have to declare their interest to join as co-authors during the same 14-day period during which also the Dataset Custodians have to respond to data requests. Their declaration should contain a specification why they are interested in the particular paper project and what they could contribute, and should be sent to the Executive Committee. For the combined EDGG Field Workshop datasets, the Governing Board will nominate at maximum as many opt-in authors as such datasets


are included in the request, based on expressed interests of the Workshop participants, taking into account their amount of contribution and how often they had been nominated in the past. Additionally, the Governing Board can nominate up to two opt-in authors from the Governing Board members or additional persons involved in the database management. For each other dataset that contributes at least 2% of the data of the particular study, the respective Dataset Custodian can nominate one of the data owners of the dataset as opt-in author. If someone is Dataset Custodian in several smaller datasets that together reach the 2% threshold, this is also accepted. The Executive Committee collects the information on proposed opt-in authors and will send them upon approval to the lead author of the publication for inclusion into the preliminary author list. Any other member of the GrassPlot Consortium not covered by the previous criteria (persons without data contribution or those whose data are not used in the particular study, data owners of datasets smaller than 2% or additional candidates from bigger datasets) can also apply to become opt-in author, but it is upon the discretion of the lead author(s) of the paper project whether they accept such an offer or not. Generally, the lead author(s) of a paper project decide when and how to involve the approved opt-in authors. However, they are requested to provide all potential co-authors adequate opportunity and time to contribute. This means that outcomes should be shared with the full author team at least at three points of time, i.e. (i) when there is a concrete outline of the methodological approach, (ii) when there are the first results and (iii) at least two weeks before submission of a planned paper or presentation of the results on a conference. If a person preliminarily listed as opt-in author, despite requests from the lead author, has not made any intellectual contribution to the paper by the time of submission, the lead author should remove the respective name after consultation with the Governing Board. In case of dispute over authorship, both lead authors and opt-in authors can appeal to the Governing Board.

8. Relationship of GrassPlot to GIVD, EVA and sPlot

GrassPlot is registered in the Global Index of Vegetation-Plot Databases (GIVD) under ID EU-00-003. The GrassPlot Governing Board regularly updates the information in GIVD and intends to prepare a GIVD Long Database Report for the journal Phytocoenologia shortly after foundation. Whenever GrassPlot data are used, as a minimum this Long Database Report has to be cited. GrassPlot is collaborating both with the European Vegetation Archive (EVA) and the global plot database “sPlot”. For those data that are suitable for EVA and sPlot, i.e. typically those with plot sizes from 1−100 m² and with cover estimates, GrassPlot aims at facilitating the data provision to these continental and global plot databases. For this purpose, GrassPlot keeps record which of its contained data are already in EVA or sPlot and which additionally would be suitable. For the non-contained but suitable data from Europe (in the sense of EVA), GrassPlot will encourage and support data provision to a suitable regional or national EVA member database. For suitable data from outside Europe that are not yet contained in sPlot, GrassPlot will ask the Dataset Custodians for their agreement to contribute their data via GrassPlot to the next release of sPlot. Note that in this case the rules of data use follow the Governance and Data Property Rules of the sPlot Working Group (http://www.idiv-biodiversity.de/sdiv/workshops/workshops-2013/splot/join/content_815683/sPlot-Rules_approved.pdf), which can deviate from the rules applicable for data directly retrieved from GrassPlot.

http://www.idiv-biodiversity.de/sdiv/workshops/workshops-2013/splot/join/content_815683/sPlot-Rules_approved.pdf

http://www.idiv-biodiversity.de/sdiv/workshops/workshops-2013/splot/join/content_815683/sPlot-Rules_approved.pdf


Supplement S2. Overview of the datasets in GrassPlot v. 1.00. The references to the quotations can be found in Supplement S3. #nested plot series refers to nested plot series with at least four different grain sizes.

Dataset ID Short dataset name Country/ies Province: location Data owner(s) Reference(s) # plots # nested plot series

EDGG Expeditions/Field Workshops: BG_A EDGG Bulgaria Bulgaria NW Bulgaria: Balkan

Range and Sredna gora Mts.

Iva Apostolova, Steffen Boch, Jürgen Dengler, Anna Ganeva, Hristo Pedashenko, Kiril Vassilev

Pedashenko et al. (2013) 277 15

ES_A EDGG Navarre Spain Navarre Itziar García-Mijangos, Asun Berastegi, Idoia Biurrun, Jürgen Dengler, Javier Etayo, Ute Jandt, Rayna Natcheva

Biurrun et al. (2014) 504 35

GR_A EDGG Greece Greece N Greece Ioannis Tsiripidis, Steffen Boch, Jürgen Dengler, Georgios Fotiadis, Chrisoula Pirini

Dengler & Demina (2012)

185 14

IT_A EDGG Sicily Italy Sicily Riccardo Guarino, Thomas Becker, Iwona Dembicz, Jürgen Dengler, Christian Dolnik, Gian Pietro Giusso del Galdo

Guarino et al. (2012) 298 21

IT_L EDGG Apennines Italy Central Apennines Goffredo Filibeck, Laura Cancellieri, Leonardo Rosati, Marta Gaia Sperandii

317 20

PL_A EDGG Poland Poland Southern and central-eastern Poland

Zygmunt Kącki, Idoia Biurrun, Marta Czarniecka-Wiera, Iwona Dembicz, Monika Staniaszek-Kik, Grzegorz Swacha, Piotr T. Zaniewski

458 31

RO_A EDGG Transylvania Romania Transylvania: Transylvanian Plateau

Eszter Ruprecht, Thomas Becker, Jürgen Dengler, Christian Dolnik, Anna Szabó

Dengler et al. (2009, 2012); Turtureanu et al. (2014)

302 20

RS_A EDGG Serbia Serbia various regions Zora Stevanovic-Dajic, Svetlana Aćić, Idoia Biurrun, Steffen Boch, Jürgen Dengler, Martin Magnes, Marko Sabovljević

Krstivojević Ćuk et al. (2015); Aćić et al. (2017)

493 32

RU_A EDGG Khakassia Russia Southern Siberia: Khakassia

Nikolai Ermakov, Iwona Dembicz, Jürgen Dengler, Goffredo Filibeck, Monika Janišová, Lukasz Kozub, Mariya A. Polyakova

Janišová et al. (2013); Polyakova et al. (2016)

561 39

UA_A EDGG Podolia Ukraine Vinnytsia region: Central Podolia

Anna Kuzemko, Thomas Becker, Jürgen Dengler, Yakiv P. Didukh, Christian Dolnik, Kiril Vassilev

Kuzemko et al. (2014, 2016)

457 21


GLORIA: AT_B GLORIA Schrankogel Austria Tyrol: Stubaier Alps: Mt.

Schrankogel AT-SCK (GLORIA)

Harald Pauli, Manfred Bardy-Durchhalter, Manuela Winkler

Gottfried et al. (1999, 2011); Pauli et al. (2007)

1055 0

AT_C GLORIA Hochswab Austria Styria: Hochschwab AT-HSW (GLORIA)

Harald Pauli, Manfred Bardy-Durchhalter, Manuela Winkler

Gottfried et al. (2012); Pauli et al. (2012); Winkler et al. (2016)

11510 59

ES_J GLORIA Ordesa Spain Pyrenees: Ordesa ES-CPY (GLORIA)

José Luis Benito Alonso, Luis Villar Gottfried et al. (2012); Pauli et al. (2012); Winkler et al. (2016)

12480 64

ES_K GLORIA South Pyrenees Spain Pyrenees: ES-SPY (GLORIA)

José Luis Benito Alonso, Luis Villar Gottfried et al. (2012); Pauli et al. (2012); Winkler et al. (2016)

64 0

ES_L GLORIA Moncayo Spain Northern Iberian Range: Moncayo ES-MON (GLORIA)

José Luis Benito Alonso, Luis Villar Winkler et al. (2016) 64 0

ES_M GLORIA Sistema Central Spain Central Range: ES-SIC (GLORIA)

Rosario G. Gavilán Gottfried et al. (2012); Pauli et al. (2012); Winkler et al. (2016)

6256 32

ES_N GLORIA Sierra Nevada East Spain Sierra Nevada: Sierra Nevada East ES-SNE (GLORIA)

Maria Rosa Fernández Calzado, Joaquín Molero Mesa


12480 64

ES_O GLORIA Sierra Nevada North Spain Sierra Nevada: Sierra Nevada North ES-SNN (GLORIA)

Maria Rosa Fernández Calzado, Joaquín Molero Mesa


12480 64

IR_C GLORIA Alborz Iran Alborz Mts.: IR-ALT (GLORIA)

Jalil Noroozi Gottfried et al. (2012); Pauli et al. (2012); Winkler et al. (2016)

12480 64

IT_O GLORIA Dolomites Italy Trentino-South Tyrol: Dolomites IT_ADO (GLORIA)

Brigitta Erschbamer Erschbamer et al. (2011); Gottfried et al. (2012); Pauli et al. (2012); Winkler et al. (2016)

11700 60

NO_B GLORIA Norway Norway Sør-Trondelag and Oppland: Dovrefjell National Park NO-DOV (GLORIA)

Pieter De Frenne, Bente J. Graae, Magni Olsen Kyrkjeeide, Ottar Michelsen, Thomas Vanneste

Gottfried et al. (2012); Pauli et al. (2012); Winkler et al. (2016); Vanneste et al. (2017)

12480 64

Individually contributed datasets: AM_A Dembicz Armenia Armenia Tavush Province:

surroundings of Dilijan Iwona Dembicz 13 1

AT_A Jentsch Austria Austria Tyrol: Pitztal: above Piller meadows

Anke Jentsch Fraser et al. (2015) 534 6

BY_A Dembicz Belarus Belarus Southern Belarus: Polesia Iwona Dembicz 9 0 CH_A Jeschke Merishausener Gräte Switzerland Canton of Zürich:

Merishausener Gräte Nature Reserve

Michael Jeschke, Kathrin Kiehl Jeschke & Kiehl (2008) 228 12


CH_B Bergamini Switzerland Switzerland whole country Ariel Bergamini, Klaus Ecker Bergamini et al. (2013, 2016); Tillé & Ecker (2014)

2079 0

CN_A Wang Tibet China Tibet: Tibetan plateau Yun Wang, Karsten Wesche Wang et al. (2017) 173 0 CN_B Baranova Qilian mountains China Gansu Province: Qilian

Mountains: Pailugou & Dayekou catchments and surroundings

Alina Baranova Baranova et al. (2016, under review)

64 0

CN_C Zhang Tibet China Tibetan plateau: eastern Qinghai

Hui Zhang Zhang (2013) 798 1

CZ_A Dengler White Carpathians Czech Republic White Carpathians: Čertoryje National Nature Reserve

Jürgen Dengler 7 1

CZ_B Hajek Hute Nature Reserve Czech Republic White Carpathians: Hutě Nature Reserve

Michal Hájek Hájek et al. (2017) 47 0

CZ_C Roleček White Carpathians Czech Republic White Carpathians: Zahrady pod Hájem National Nature Reserve

Jan Roleček, Zuzana Plesková Roleček et al. (2012) 21 0

CZ_D Hajek Klimes Czech Republic White Carpathians: Čertoryje National Nature Reserve

Michal Hájek Klimeš (1997) 308 28

CZ_I Leps Southern Bohemia Czech Republic Southern Bohemia: near the village Ohrazení

Jan Lepš Lepš (2014) 372 12

DE_A Dengler Upper Franconia Germany Bavaria: Schlehenmühle near Bayreuth

Jürgen Dengler Hopp & Dengler (2015) 13 1

DE_B Dengler BR Schorfheide-Chorin

Germany Brandenburg: Biosphere Reserve Schorfheide-Chorin: Gabow - Altglietzen

Jürgen Dengler Dengler et al. (2004) 750 10

DE_C Dengler Wadden Sea Germany Schleswig-Holstein: North Sea coast

Jürgen Dengler, Kai Jensen 695 67

DE_D Dengler Lenzen Germany Brandenburg: Lenzen: Rückdeichungsgebiet

Jürgen Dengler, Kai Jensen Jensen et al. (2013) 243 27

DE_E Dengler BR Middle Elbe Germany Lower Saxony: Biosphere Reserve Middle Elbe: Höhbeck

Jürgen Dengler, Oliver Schuhmacher Schuhmacher & Dengler (2013)

194 0

DE_F Dengler Bayreuth Germany Bavaria: Bayreuth: University Campus and Ecological Botanical Garden

Jürgen Dengler Dengler (2016) 234 18

DE_G Kiehl Hamburger Hallig salt marshes

Germany Schleswig-Holstein: Hamburger Hallig saltmarshes

Kathrin Kiehl Wanner et al. (2014) 376 47

DE_H Langer Bayreuth Germany Bavaria: surroundings of Bayreuth

Nancy Langer, Julia Went Langer (2016); Went (2016)

286 22


DE_I Jeschke Garchinger Heide Germany Bavaria: Garchinger Heide Nature Reserve

Michael Jeschke, Kathrin Kiehl Kiehl & Jeschke (2005); Jeschke & Kiehl (2006); Jeschke (2008)

620 32

DE_J Jeschke Kissinger Heide Germany Bavaria: Kissinger Heide Nature Reserve

Michael Jeschke, Kathrin Kiehl Jeschke (2008) 164 8

DE_K Allers Lüneburg Germany Lower Saxony: Topographic map sheet 2728 (Lüneburg)

Marc-Andre Allers Dengler & Allers (2006); Allers (2007)

110 12

DE_L Dengler Uckermark Germany Brandenburg: Biosphere Reserve Schorfheide-Chorin and National Park Lower Odra Valley

Jürgen Dengler Langer et al. (2017) 322 23

DE_M Dengler Brodowin Germany Brandenburg: Topographic map sheet 3049 (a 3 km x 2 km sector around the village of Brodowin)

Jürgen Dengler Dengler & Allers (2006) 68 5

DE_N Diekmann Lower Saxony Germany Lower Saxony: southern parts

Martin Diekmann, Cecilia Dupré Diekmann et al. (2014) 125 0

DE_O Conradi Southern Bavaria Germany Bavaria: southern parts Timo Conradi Conradi & Kollmann (2016); Conradi et al. (2017)

296 0

DE_P Hobohm Lower Saxony Germany Lower Saxony: districts of Lüneburg and Lüchow-Dannenberg

Carsten Hobohm Hobohm (1998) 30 0

DE_Q Hüllbusch Gabower Hänge Germany Brandenburg: Gabower Hänge

Elisabeth M. Hüllbusch Hüllbusch et al. (2016) 16 0

DE_R Suchrow Wadden Sea Germany Schleswig-Holstein: Wadden Sea National Park: districts of North Frisia and Dithmarschen

Sigrid Suchrow, Kai Jensen, Marin Stock

Suchrow & Jensen (2010); Suchrow et al. (2012, 2015); Wanner et al. (2014)

2691 0

DK_A Bruun Denmark Denmark Central Zealand: Ejby and Kyndby

Hans Henrik Bruun Bruun (1998) 84 0

EE_A Boch Saaremaa Estonia Western Estonia: Saaremaa

Steffen Boch Boch (2005); Boch & Dengler (2006); Dengler & Boch (2008)

576 16

EE_B Helm Alvar restoration Estonia Western Estonia: Saaremaa, Hiiumaa and Muhu islands

Aveliina Helm, Nele Ingerpuu Helm (2017); Prangel (2017)

248 62

ES_B Rodriguez-Rojo SW Iberian System

Spain Cuenca, Guadalajara, Teruel Provinces: Southwestern Iberian System

Maria Pilar Rodríguez-Rojo Rodríguez-Rojo et al. (2012, 2013)

141 0

ES_C Chocarro Pyrenees Spain Aragón: Pyrenees (six valleys in southern Pyrenees)

Cristina Chocarro Reiné et al. (2009, 2014); Chocarro et al. (2010)

160 0


ES_D Campos Dunes Bizkaia Spain Basque Country: Gorliz, Muskiz

Daniel García-Magro, Juan Antonio Campos

Herrera et al. (2016) 660 0

ES_E Campos Gorbea Spain Basque Country: Gorbea mountain

Juan Antonio Campos, Daniel García-Magro, Itziar García-Mijangos

García-Mijangos et al. (2016)

60 0

ES_F Campos Basque mires Spain Basque Country and Navarre: Cantabrian mountains

Juan Antonio Campos, Idoia Biurrun Campos & Biurrun (2015)

67 0

ES_G Luzuriaga Aranjuez Spain Central Iberian Peninsula: Aranjuez

Arantzazu L. Luzuriaga, Adrián Escudero, Ana María Sánchez

Luzuriaga et al. (2012); Peralta et al. (2016)

1572 12

ES_H Luzuriaga Belinchon Spain Central Iberian Peninsula: Belinchón

Arantzazu L. Luzuriaga, Adrián Escudero, Ana María Sánchez

Luzuriaga et al. (under review)

500 0

ES_I Luzuriaga Titulcia-Ocaña Spain Central Iberian Peninsula: Titulcia-Ocaña

Arantzazu L. Luzuriaga, Adrián Escudero, Jose M. González

Luzuriaga et al. (2015) 4620 35

ES_P Alfaro Picos de Europa Spain Asturias and Cantabria: Picos de Europa

Borja Jiménez-Alfaro, Alvaro Bueno, Corrado Marcenò

Jímenez-Alfaro et al. (2010)

16 0

EU_A Hajek West Carpathian fens Czech Republic, Slovakia

West Carpathians Zuzana Fajmonová, Michal Hájek, Petra Hájková

Rozbrojová (2005); Rozbrojová & Hájek (2008)

106 0

EU_B Hajek White Carpathian grasslands

Czech Republic, Slovakia

White Carpathians: SW and central part of the White Carpathians

Zuzana Fajmonová, Michal Hájek Fajmonová & Hájek (2011)

50 0

EU_C Hajek spring fen nested series Czech Republic, Poland, Slovakia

West Carpathians Eva Hettenbergerová, Zuzana Fajmonová, Michal Hájek, Petra Hájková, Zuzana Plesková

Hájková & Hájek (2003); Náhlíková (2009)

613 75

EU_D Reitalu Baltic Sea Region Estonia, Russia, Sweden

Baltic Sea Region Triin Reitalu, Karin Bengtsson, Aveliina Helm, Meelis Pärtel, Honor C. Prentice, Ejvind Rosén, Sergey Znamenskiy

Bengtsson et al. (1988); Znameskiy et al. (2006); Reitalu et al. (2014)

1223 0

EU_E Roleček Hungary-Romania Hungary, Romania Northern Hungary, Transylvania: Mátra Mts., Bükk Mts., Transylvania

Jan Roleček Roleček et al. (2016) 4 0

EU_F Torca Bay of Biscay dunes France, Spain SW France, N Spain: Coasts of Bay of Biscay, from Galicia to Landes

Marta Torca, Juan Antonio Campos, Mercedes Herrera

Torca (2015) 3312 144

EU_G Palpurina Bulgaria and Romania

Bulgaria, Romania Danube plain, Dobrudzha plataeu, Strandzha Mts., W Balkan Mts., SW Bulgaria

Salza Palpurina, Irena Axmanová, Milan Chtyrý, Jiří Danihelka, Rossen Tzonev

Palpurina et al. (2015, 2017); Wagner et al. (2017)

329 0

EU_H Virtanen Svalbard and Lapland

Finland, Svalbard Jehkats, Svalbard and Finish Lapland: Sveagruva region (Svalbard) and NW Finnish Lapland

Risto Virtanen Virtanen (2018) 30 0


EU_I Pätsch Baltic Sea salt marshes Germany, Poland Baltic Sea Region: Michaelsdorf, Karrendorfer- und Kooser Wiesen, Großer Wotig (Pennemündung) and Karsiborska Kepa, Chrzaszczewska Wyspa, Wlodarka, Beka

Ricarda Pätsch Pätsch (2014) 81 0

FR_A Gillet Jura France Jura Mts.: Doubs and Jura François Gillet, Leslie Mauchamp Mauchamp et al. (2014, 2016)

816 48

HR_A Vitasovic Croatia Croatia Istria: Ćićarija Mt. Ivana Vitasović Kosić Vitasović Kosić et al. (2011, 2012, 2014a,b)

98 0

HU_A Tölgyesi Central Hungary Hungary Central Hungary: lowlands of the Turjánvidék

Csaba Tölgyesi Tölgyesi et al. (2016) 520 0

HU_B Bartha Hungary sandy grasslands

Hungary Kiskunság: Fülöpháza Sándor Bartha Bartha (2016) 1180 5

HU_C Török Hortobágy-Kiskunság National Parks

Hungary Central and Eastern Hungary: Hortobágy and Kiskunság National Parks

Orsolya Valkó, Balázs Deák, András Kelemen, Péter Török, Béla Tóthmérész

Török et al. (2010, 2014); Deák et al. (2014, 2015); Valkó et al. (2016, 2017)

568 0

HU_D Török nested series Hungary Eastern Hungary: Hungarian Great Plain

András Kelemen, Orsolya Valkó, Balázs Deák, Péter Török, Béla Tóthmérész

Godó et al. (2017) 300 30

HU_E Molnár Kiskunsag Hungary Great and Small Hungarian Plains: Kiskunsag sand area

Zsolt Molnár Fekete et al. (2002) 1078 0

IL_A Giladi Southern Judea Israel Southern Judea lowland: Dvir, Lachish and Gal On

Itamar Giladi, Florian Jeltsch, Felix May, Yaron Ziv

Giladi et al. (2011) 1795 82

IR_A Naqinezhad Central Alborz Iran Alborz Mts.: Central Alborz, Damavand

Alireza Naqinezhad, Amir Talebi Talebi (2017) 357 21

IR_B Noroozi Alborz Iran Alborz and NW Iran Jalil Noroozi Noroozi (2013); Noroozi et al. (2010, 2014, 2017)

155 0

IT_B Filibeck Abruzzo Italy Apennines: Abruzzo NP Goffredo Filibeck, Laura Cancellieri Filibeck et al. (2015); Cancellieri et al. (2017)

567 0

IT_C Baumann Gran Paradiso Italy Aosta Valley: Gran Paradiso National Park

Esther Baumann, Frank Weiser Baumann et al. (2016) 182 14

IT_D Dengler Aosta Italy Aosta Valley: Cogne: S-facing slopes above village

Jürgen Dengler Wiesner et al. (2015) 26 2

IT_E Terzi Alta Murgia Italy Apulia: Alta Murgia Massimo Terzi, Mariano Fracchiolla Ciani et al. (2012) 70 5 IT_F Maccherini Monte Labbro Italy Tuscany: Monte Labbro Simona Maccherini Maccherini et al. (2007) 29 0 IT_G Maccherini Maremma Italy Tuscany: Parco Regionale

della Maremma Simona Maccherini, Ilaria Bonini Maccherini et al. (2013) 56 0


IT_H Chiarucci Parco della Chiusa Italy Northern Italy: Parco della Chiusa (Casalecchio di Reno, Bologna, Emilia Romagna Region)

Alessandro Chiarucci, Chiara Lelli Suanno (2017) 78 6

IT_I Chiarucci Radicondoli Italy Siena Region: village Radicondoli

Alessandro Chiarucci, Daniele Viciani Chiarucci et al. (2006) 111 3

IT_J Maccherini Crete Senesi Italy Tuscany: Crete Senesi Simona Maccherini Maccherini et al. (2011); Torri et al. (2013)

111 0

IT_K Viciani Tuscan Apennines Italy Tuscany: near Passo di Viamaggio, Badia Tedalda (Tuscan-Romagna Apennines), at altitudes around 1000 m a.s.l.

Daniele Viciani, Lorenzo Lastrucci, Lorenzo Lazzaro

Lazzaro et al. (under review)

60 0

IT_M Conti Lazio dunes Italy Lazio Region: Montalto Marina

Luisa Conti, Alicia T.R. Acosta, Marta Carboni

Conti et al. (2017) 1302 42

IT_P Chiarucci Siena Italy Tuscany: Siena Sara Landi, Alessandro Chiarucci Chiarucci et al. (2012); Amici et al. (2013)

36 0

JP_A Atushi Japan Japan Nagano Prefecture: Kaida plateau

Atushi Ushimaru, Yuko K. Nagata Nagata & Ushimaru (2016)

90 0

JP_B Koyama Japan Japan Nara, Kanagawa, Gunma, Kumamoto: Soni, Sengokuhara, Minakami, Aso

Asuka Koyama, Munemitsu Akasaka, Tomoyo F. Koyanagi

Koyama et al. (2017, 2018)

328 0

LV_A Rusina Latvia Latvia whole country Solvita Rusina Rūsiņa (2016) 250 0 MA_A Finckh southern Morocco Morocco southern Morocco Manfred Finckh 32 0 MN_A Wesche southern Mongolia Mongolia southern Mongolia: Gobi

Gurvan Saykhan National Park and surroundngs

Karsten Wesche Wesche et al. (2005); von Wehrden et al. (2009)

334 0

MN_B Kanikuma Bulgan Mongolia South Gobi Province: Bulgan

Kaoru Kakinuma Kakinuma et al. (2017) 80 0

MN_C Kanikuma Madalgobi Mongolia Central Gobi: Madalgobi Kaoru Kakinuma Kakinuma et al. (2017) 60 0 NL_A Peet Limburg Netherlands Southern Limburg:

Wrakelberg Nature Reserve

Robert K. Peet Willems et al. (1993); Sykes et al. (1994)

354 3

NO_A Grytnes central Norway Norway central Norway: Jotunheimen, Sikkilsdalen

John-Arvid Grytnes, Vivian A. Felde, Jutta Kapfer

Felde et al. (2012); Kapfer et al. (2013)

237 0

PL_B Pielech Karkonosze Mts. Poland SW Poland: Karkonosze Mts.

Remigiusz Pielech, Marek Malicki Malicki & Pielech (2016)

88 0

PL_C Czarniecka Lower Silesia Poland Lower Silesia: Sudety Mts.

Marta Czarniecka-Wiera, Zygmunt Kącki

2221 370

RO_B Mardari Moldavian Plateau Romania Moldova: Moldavian Plateau

Constantin Mardari, Cătălin Tănase Mardari & Tănase (2016)

585 45

RO_C Germany southern Transylvania

Romania southern Transylvania Markus Germany, Thomas Becker, Ute Becker, Laura M.E. Sutcliffe

Sutcliffe et al. (2016) 102 0


RU_G Dolnik Curonian Spit Russia Kaliningrad Oblast: Curonian Spit and Sambia Peninsula

Christian Dolnik Dolnik (2003, 2006) 832 64

RU_I Belonovskaya Novgorodskaya Russia Novgorodskaya oblast: Valday hills

Elena Belonovskaya, Nadezda Tsarevskaya

Belonovksaya & Tsarevskaya (2017)

10 1

RU_J Reinecke Yakutia Russia Sakha Republic (Yakutia): Yakutsk, Verkhoyansk, Chersky

Jennifer Reinecke, Karsten Wesche Reinecke et al. (2017) 185 0

SE_A Löbel Öland Sweden Öland: Southern Öland Swantje Löbel Löbel (2002); Löbel et al. (2006); Löbel & Dengler (2008)

341 31

SE_B Diekmann Southern Sweden Sweden Uppland and Öland Martin Diekmann, Cecilia Dupré Dupré & Diekmann (2001)

989 62

SE_C Peet Öland Sweden Öland: Gettlinge, Kleva and Skarpa Alby

Robert K. Peet, Eddy van der Maarel, Ejvind Rosén

Sykes et al. (1994); Wilson et al. (1995)

768 6

SE_D Reitalu Öland Sweden Öland: Jordtorp and three adjacent villages

Triin Reitalu, Honor C. Prentice, Martin Sykes

Reitalu et al. (2008, 2009, 2010, 2012)

23736 516

SI_A Pipenbaher North Adriatic Karst

Slovenia sub-mediterranean belt: North Adriatic Karst, Crnotice

Nataša Pipenbaher, Branko Bakan, Sonja Škornik

Batalha et al. (2015) 37 0

SJ_A Kapfer Jan Mayen Svalbard and Jan Mayen

Jan Mayen Jutta Kapfer, John-Arvid Grytnes Kapfer et al. (2012) 254 0

TR_A Kavgacı İğneada dunes Turkey Thracia: İğneada (Kırklareli)

Ali Kavgacı Kavgacı (2007) 30 0

UA_B Savchenko Dvorichanskyi Ukraine Kharkiv region: National Nature Park “Dvorichanskyi

Galina Savchenko, Vladimir Ronkin Ronkin & Savchenko (2016)

57 0

UA_C Roleček Chernivtsi Ukraine Bukovinian Pre-Carpathians: Spas'ka near Chernivtsi

Jan Roleček Roleček et al. (2014) 4 0

UA_D Janišová Chywchyny Mts. Ukraine Carpathians: Chywchyny Mts.

Monika Janišová, Vasyl Budzhak, Anna Kuzemko

Janišová et al. (2016) 65 5

UA_E Dembicz Kherson region Ukraine Kherson region: Lower Dnipro Valley, Askania Nova Biosphere Reserve

Iwona Dembicz, Ivan Y. Moysiyenko Dembicz et al. (2016) 1034 0

UA_F Vasheniak Ukraine Ukraine Khmelnytska, Ternopilska, Ivano-Frankivska and Chernivetska regions: Dniester Canyon and some parts of its tributaries (Zolota Lypa, Smotrych).

Iuliia Vashenyak Vasheniak (2016) 214 0


UA_G Savchenko Velykyi Burluk-Steppe

Ukraine Kharkiv region: Regional Landscape Park “The Velykyi Burluk-Steppe” near Nerterivka village

Galina Savchenko, Vladimir Ronkin Ronkin & Savchenko (2016)

60 0

UK_A Archibold Great Britain United Kingdom England: various locations Idoia Biurrun Archibald (1949) 66 10 UK_B Pakeman Outer Hebrides United Kingdom Western Isles: Outer

Hebrides of Scotland Robin J. Pakeman, Hannah White White et al. (2018) 2820 30


Supplement S3. Bibliographic references to the datasets contained in GrassPlot 1.00.

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Baranova, A., Oldeland, J., Wang, S. & Schickhoff, U. (under review). Heavy grazing impacts feed values and macronutrient content of mountain grasslands in Qilian Shan, NW China. (Under review in Land Degradation & Development).

Bartha, S. 2016. Unpublished data from the project NKFI 105608 "Diversity, regulation and reliability – fine-scale functional organization in vegetation, 2013-2017", Objective #1 (Comparing the efficiency of different sampling designs for representing fine-scale spatial organization in grasslands). MTA Centre for Ecological Research, Vácrátót, HU.

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Dengler, J., Becker, T., Ruprecht, E., Szabó, A., Becker, U., Beldean, M., Bita-Nicolae, C., Dolnik, C., Goia, I., (…) & Uǧurlu, E. 2012. Festuco-Brometea communities of the Transylvanian Plateau (Romania): a preliminary overview on syntaxonomy, ecology, and biodiversity. Tuexenia 32: 319-359.

Diekmann, M., Jandt, U., Alard, D., Bleeker, A., Corcket, E., Gowing, D. J., Stevens, C.J. & Duprè, C. 2014. Long-term changes in calcareous grassland vegetation in North-western Germany – No decline in species richness, but a shift in species composition. Biological Conservation 172: 170–179.

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Dolnik, C. 2006. Artenreichtum in Küsten-Sandtrockenrasen der Kurischen Nehrung und des Samlandes auf unterschiedlichen räumlichen Skalenebenen. Arbeiten aus dem Institüt für Landschaftsökologie Münster 15: 83–95.

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Fajmonová, Z. & Hájek, M. 2011. Unpublished data from the study “Diversity of grasslands related to soil conditions”. Masaryk University, Brno, CZ.

Fekete, G., Molnár, Z., Kun, A., & Botta-Dukát, Z. 2002. On the structure of the Pannonian forest steppe: grasslands on sand. Acta Zoologica Academiae Scientiarum Hungaricae 48: 137-150.

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Fraser, L.H., Pither, J. Jentsch, A., Sternberg, M., Zobel, M., Askarizadeh, D., Bartha, S., Beierkuhnlein, C., Bennett, J.A., (…) & Zupo, T. 2015. Worldwide evidence of unimodal relationship between productivity and plant species richness. Science 349: 302–305.

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Supplement S4. Overview of the content of the header data fields other than those in Tables 1–4 and Fig. 2.

Table S4.1. Amount of compositional, structural and environmental data that have been provided but not integrated, promised but not provided yet and integrated into GrassPlot 1.00. The percentage is referred to the total data.

Available Data Provided Promised Integrated Composition 95.1% 0.2% 2.6% Structure 0.3% 0.1% 80.0% Topography 23.4% 0.1% 58.2% Soil 12.3% 0.7% 1.9% Biomass 0.7% 0.3%

Table S4.2. Overview of methodological, structural and ecological parameters stored in GrassPlot. The column “Available” indicates the fraction of plots in GrassPlot for which the respective field is filled in version 1.00. “Distribution of parameter values” refers to the total data as 100%.

Parameter Available Distribution of parameter values

Species composition data 98.9% Cover (18.5%); presence-absence (79.9%); abundance (= number of individuals) (0.5%)

Land use 89.1% Natural (60.6%); Grazed (19.6%); Mown (3.8%); Abandoned (3.8%); Fallow fields (0.9%); Burning (0.4%)

Structural data Available Median Max Min Total vegetation cover (%) 6.9% 75.0 100.2 0.0 Tree and shrub cover (%) 54.7% 0.0 267.9 0.0 Cover tree layer (%) 59.3% 0.0 75.0 0.0 Cover shrub layer (%) 41.4% 0.0 100.0 0.0 Cover herb layer (%) 8.9% 33.0 120.0 0.0 Cover cryptogam layer (%) 7.0% 0.0 100.0 0.0 Cover litter (%) 4.4% 10.0 100.2 0.0 Cover dead wood (%) 1.7% 0.0 16.0 0.0 Cover stones and rocks (>63 mm) (%)

4.6% 0.0 100.0 0.0

Cover gravel (2-63 mm) (%) 3.0% 0.0 100.0 0.0 Cover fine soil (<2 mm) (%) 3.0% 65.0 100.0 0.0 Maximum height of herb layer (cm)

2.2% 60.0 288.0 0.0

Mean height of herb layer (cm) 2.6% 12.8 180.0 0.1

Biomass Available Median Max Min Living Biomass 0.3% 328.1 7351.0 40.4 Dead biomass 0.2% 8.7 144.7 0.2

Topography Aspect (º or verbal) 52.4% 180.0 374.0 0.0 Inclination (º) 57.9% 16.0 90.0 0.0 Heat.load Index 0.1% 0.5 0.8 -0.5 Max. microrelief (cm) 1.6% 17.0 170.0 0.0

Soil Soil depth mean (cm) 2.4% 9.9 100.0 0.0 Skeleton content (mass of particles % >2mm)

0.2% 3.7 47.4 0.0

Loss at ignition (%) 0.1% 7.8 35.8 0.5 pH (H2O) 0.9% 7.5 9.8 4.2 pH (CaCl2) 0.5% 7.1 10.0 4.8 pH (KCl) 0.0% EC (µS/cm) 0.4% 185.5 31900.0 20.0 Soil N content (%) 0.8% 0.4 2.8 0.0 Soil C content (%) 0.7% 7.4 30.4 0.9 C/N ratio 0.4% 11.6 3.09 8.8 CaCO3 (% and ordinal) 0.3% 6.0 10.0 0.0 TEB : Total Exchangable Bases(Cmol/Kg)

0.0% - - -

CEC: Cation Exchangable capacity (cmol/Kg)

0.0% - - -

Base saturation (%) 0.03% 99.6 99.8 98.9 Organic matter 0.03% 10.8 26.7 3.4 Soil water content (%) 0.1% 27.1 67.2 1.2 Soil organic C (%) 0.4% 1.6 28.1 0.2 Available P (mg/100g) 0.1% 1.7 2.3 1.1

Available K (mg/100g) 0.1% 43.3 68.5 14.0 Soil texture class 0.0% - - -

Fig. S4.1. Diagram of soil data of GrassPlot 1.00. Numbers are the frequency (%) of data available for each parameter related to total data.

Fig. S4.2. Diagram of land use data in GrassPlot 1.00. Numbers are the frecuency (%) of data for each category related to total data as 100% (including NA: 10.9%).

2.4 0.2

0.1 0.9

0.4 0.8

0.7 0.4

0.3 0.03 0.03

0.1 0.4

0.1 0.1

Soil depth mean (cm)Skeleton content

loss at ignition (%)pH (H2O and CaCl2)

EC (µS/cm)Soil N content (%)Soil C content (%)

C/N ratioCaCO3 (% and ordinal)

Base.saturation %Organic matter

Soil water content (%)soil organic C (%)

Available P (mg/100g)Available K (mg/100g)

Soil

60.6

19.6

3.8 3,8

0.9 0.4

Land use

Natural

Grazed

Abandoned

Mown

Fallow fields

Burning

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