# Disturbance indicator values for European plants

Gabriele Midolo, Tomáš Herben, Irena Axmanová, Corrado Marcenò, Ricarda Pätsch, Helge Bruelheide, Dirk Nikolaus Karger, Svetlana Aćić, Ariel Bergamini, Erwin Bergmeier, et al.

► To cite this version:

Gabriele Midolo, Tomáš Herben, Irena Axmanová, Corrado Marcenò, Ricarda Pätsch, et al. Disturbance indicator values for European plants. Global Ecology and Biogeography, 2023, 32 (1), pp.24-34.
10.1111/geb.13603. hal-04259803

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**Title:** Disturbance indicator values for European plants

**Keywords:** bioindicators; ecological niche; Ellenberg values; expert judgement; functional trait; plant life form

**Authors:** Midolo, Gabriele<fnref n="1" />*, Herben, Tomáš<fnref n="2" />,<fnref n="3" />, Axmanová, Irena<fnref n="1" />, Marcenò, Corrado<fnref n="1" />, Pätsch, Ricarda<fnref n="1" />, Bruelheide, Helge<fnref n="4" />,<fnref n="5" />, Karger, Dirk Nikolaus<fnref n="6" />, Aćić, Svetlana<fnref n="7" />, Bergamini, Ariel<fnref n="8" />, Bergmeier, Erwin⁹, Biurrun, Idoia¹⁰, Bonari, Gianmaria¹¹, Čarni, Andraž¹²,¹³, Chiarucci, Alessandro¹⁴, De Sanctis, Michele¹⁵, Demina, Olga¹⁶, Dengler, Jürgen<fnref n="6" />,¹⁷,¹⁸, Dziuba, Tetiana¹⁹, Fanelli, Giuliano¹⁵, Garbolino, Emmanuel²⁰, Giusso del Galdo, Gianpietro²¹, Goral, Friedemann²², Güler, Behlül²³, Hinojos-Mendoza, Guillermo²⁴, Jansen, Florian²⁵, Jiménez-Alfaro, Borja²⁶, Lengyel, Attila²⁷, Lenoir, Jonathan²⁸, Pérez-Haase, Aaron²⁹,³⁰, Pielech, Remigiusz³¹,³², Prokhorov, Vadim³³, Rašomavičius, Valerijus³⁴, Ruprecht, Eszter³⁵, Rusina, Solvita³⁶, Šilc, Urban³⁷, Škvorc, Željko³⁸, Stancic, Zvjezdana³⁹, Tatarenko, Irina⁴⁰, and Chytrý, Milan<fnref n="1" />

<footnote n="1">¹Department of Botany and Zoology, Faculty of Science, Masaryk University, Brno, Czech Republic</footnote>

<footnote n="2">²Department of Botany, Faculty of Science, Charles University, Prague, Czech Republic</footnote>

<footnote n="3">³Institute of Botany, Czech Academy of Sciences, Průhonice, Czech Republic</footnote>

<footnote n="4">⁴Institute of Biology/Geobotany and Botanical Garden, Martin Luther University Halle-Wittenberg, Germany</footnote>

<footnote n="5">⁵German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany</footnote>

<footnote n="6">⁶WSL Swiss Federal Institute for Forest, Snow and Landscape Research, Birmensdorf, Switzerland</footnote>

<footnote n="7">⁷Department of Botany, Faculty of Agriculture, University of Belgrade, Belgrade, Serbia</footnote>

<footnote n="8">⁸Biodiversity & Conservation Biology, WSL Swiss Federal Research Institute, Birmensdorf, Switzerland</footnote>
⁹Department of Vegetation & Phytodiversity Analysis, University of Göttingen, Germany

¹⁰Plant Biology and Ecology, University of the Basque Country UPV/EHU, Bilbao, Spain

¹¹Faculty of Science and Technology, Free University of Bozen-Bolzano, Bolzano, Italy

¹²Institute of Biology, Research Centre of the Slovenian Academy of Sciences and Arts, Ljubljana, Slovenia

¹³Faculty for Viticulture and Enology, University of Nova Gorica, Nova Gorica, Slovenia

¹⁴BIOME Lab, Department of Biological, Geological and Environmental Sciences, Alma Mater Studiorum – University of Bologna, Bologna, Italy

¹⁵Departement of Environmental Biology, Sapienza University of Rome, Italy

¹⁶Karachay-Circassian State University, Karachaevsk, Russia

¹⁷Vegetation Ecology Research Group, Institute of Natural Resource Sciences (IUNR), Zurich University of Applied Sciences (ZHAW), Wädenswil, Switzerland

¹⁸Plant Ecology, Bayreuth Center of Ecology and Environmental Research (BayCEER), University of Bayreuth, Bayreuth, Germany

¹⁹Department of Geobotany and Ecology, M.G. Kholodny Institute of Botany, National Academy of Sciences of Ukraine, Kyiv, Ukraine

²⁰Climpact Data Science (CDS), Sophia Antipolis Cedex, France

²¹Department of Biological, Geological and Environmental Sciences, University of Catania, Italy

²²Department of Vegetation & Phytodiversity Analysis, University of Göttingen, Germany

²³Biology Education, Dokuz Eylül University, Buca, İzmir, Turkey

²⁴ASES Ecological and Sustainable Services, Aubenas, France

²⁵Faculty of Agricultural and Environmental Sciences, University of Rostock, Germany

²⁶Department of Organisms and Systems Biology and Research Unit of Biodiversity (UMIB, UO-CSIC-PA), University of Oviedo, Oviedo, Spain

²⁷Centre for Ecological Research, Institute of Ecology and Botany, Vácrátót, Hungary

²⁸UMR CNRS 7058 Ecologie et Dynamique des Systèmes Anthropisés (EDYSAN) Université de Picardie Jules Verne, Amiens, France

²⁹Department of Biosciences, Faculty of Sciences and Technology, University of Vic - Central University of Catalonia, Vic, Barcelona, Spain

³⁰Department of Evolutionary Biology, Ecology and Environmental Sciences, Faculty of
Biology, University of Barcelona, Barcelona, Spain

<sup>31</sup>Department of Forest Biodiversity, Faculty of Forestry, University of Agriculture in Kraków, Poland

<sup>32</sup>Foundation for Biodiversity Research, Wrocław, Poland

<sup>33</sup>Kazan Federal University, Kazan, Russia

<sup>34</sup>Nature Research Centre, Institute of Botany, Vilnius, Lithuania

<sup>35</sup>Hungarian Department of Biology and Ecology, Babeş-Bolyai University, Cluj-Napoca, Romania

<sup>36</sup>Faculty of Geography and Earth Sciences, University of Latvia, Riga, Latvia

<sup>37</sup>ZRC SAZU, Institute of biology, Ljubljana, Slovenia

<sup>38</sup>University of Zagreb, Faculty of Forestry and Wood Technology, Zagreb, Croatia

<sup>39</sup>Faculty of Geotechnical Engineering, University of Zagreb, Varaždin, Croatia

<sup>40</sup>School of Environment, Earth and Ecosystem Sciences, STEM, Open University, United Kingdom

* Correspondence: gmidolo@sci.muni.cz

## Acknowledgements

We thank EVA database custodians who provided data on vegetation plots but did not participated as co-authors: S. Abdulhak, A. Acosta, E. Agrillo, I. Apostolova, J. Bouzillé, J. A. Campos, L. Casella, R. Cušterevska, E. De Bie, I. de Ronde, P. Dimopoulos, F. Fernández-González, Ú. Fitzpatrick, X. Font, R. G. Gavilán, J. Gégout, Y. Golovanov, V. Golub, F. Goral, S. Hennekens, A. Indreica, M. Isermann, U. Jandt, J. Jansen, J. Janssen, Z. Kacki, V. Kalníková, A. Kanerva Jašková, A. Kavgaci, L. Khanina, M. Krstivojevic Cuk, A. Kuzemko, F. Küzmic, F. Landucci, I. Lavrinenko, T. Lysenko, A. Marinšek, R. Mitchell, P. Novák, V. Onipchenko, V. Onyshchenko, R. Pakeman, T. Peterka, M. P. Rodríguez-Rojo, J. S. Rodwell, J. Schrautzer, P. Shirokikh, A. Stanisci, I. Tsiripidis, R. Tsvirko, E. Ugurlu, M. Valachovic, K. Vassilev, D. Vynokurov, W. Willner, T. Wohlgemuth and S.
Yamalov. This research was funded through the 2019-2020 BiodivERsA joint call for research proposals, under the BiodivClim ERA-Net COFUND program, and with the funding organisations Swiss National Science Foundation SNF (project: FeedBaCks, 193907), the German Research Foundation (DFG BR 1698/21-1, DFG HI 1538/16-1) and the Technology Agency of the Czech Republic (SS70010002). I.B. was supported by the Basque Government (IT936-16). S.R. was supported by LIFE19 IPE/LV/000010.

## Biosketch

Gabriele Midolo is a postdoctoral fellow interested in the response of plant communities and functional traits to environmental change and how this knowledge can be applied to aid ecosystem management and conservation.
His co-authors work on various fields of plant ecology and botany.

**NOTE for co-authors:** please note that *GEB* is double-blind, so every personal information is stored in the title page. In addition, funding information needs to be mentioned in the Acknowledgements. Please, email me (gabriele.midolo@gmail.com) your funding information to be displayed. For those whom acknowledgements are already included, please check these and report to me any issue.
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1 **Title:** Disturbance indicator values for European plants

2 **Keywords:** bioindicator; ecological niche; Ellenberg value; expert judge-

3 ment; functional trait; plant life form

4 Manuscript category: DATA ARTICLE

Abstract

6 **Motivation** Indicator values are numerical values used to characterize eco-
logical niches of species and estimate their occurrence along gradients. While
7 indicator values on climatic and edaphic niches of plant species hare received
8 considerable attention in ecological research, data on species optimal posi-
tioning along disturbance gradients are less developed. Here, we present a
10 new data set of disturbance indicator values identifying optima along gra-
dients of natural and anthropogenic disturbance for 6,382 vascular plant
12 species based on the analysis of 736,366 European vegetation plots and
13 using expert-based characterization of disturbance regimes in 236 habitat
types. The indicator values presented here are crucial for integrating distur-
bance niche optima into large-scale vegetation analyses and macroecological
studies.
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18 **Main types of variables contained** We set up five main continuous
19 indicator values for European plants: disturbance severity, disturbance fre-
quency, mowing frequency, grazing pressure and soil disturbance. The first
20 two indicators are provided separately for the whole community and the herb
21 layer. We calculated the values as the average of expert-based estimates of
22 disturbance values in all habitat types where a species occurs, weighted by
23 the number of plots in which the species occurs within a given habitat type.
24
**Spatial location and grain** Europe. Vegetation plots ranging in size from 1 m² to 1,000 m².

**Time period and grain** Vegetation plots mostly sampled between 1956 and 2013 (= 5th and 95th quantiles of the sampling year, respectively).

**Major taxa and level of measurement** Species-level indicator values for vascular plants.

**Software format** .csv file.

# 1 Introduction

Disturbance is a major driver of vegetation dynamics, influencing plant growth and species interactions (Huston 1979; Pickett & White 1985; McIntyre, Lavorel, Landsberg, & Forbes 1999), with consequences for the realized niche and distribution of species (Sheil, 2016). For practical purposes, disturbance has been defined by plant ecologists as mechanisms that limit plant biomass through its partial loss or complete destruction (Grime, 1979; van der Maarel, 1993; White & Jentsch, 2001). Thus, disturbance is thought to play a strong role in the functional differentiation of plant communities (Grime, 1979; Westoby, 1998).

In contrast to the major climatic and soil niches of species described by, for instance, Ellenberg indicator values (EIVs) (Ellenberg et al., 1992), the estimation of species disturbance optima has received less attention in the literature (but see Frank & Klotz 1990; Landolt et al. 2010; Grime, Hodgson, & Hunt 2014). Thus, to deepen our understanding of plant community responses to global environmental changes, as well as to improve disturbance-dependent ecosystem management strategies, we need to integrate analyses
49 of the disturbance niche (namely, the species optimal positioning within disturbance gradients) into ecological studies.

51 A common approach in ecology is to link species response to disturbance with morpho-physiological functional traits (Laliberté, Shipley, Norton, & Scott 2012; Vandewalle et al. 2013; Jäschke, Heberling, & Wesche 2020). For example, the ‘competitor, stress-tolerator, ruderal’ (CSR) theory has been proposed to identify main plant strategy classes based on functional traits (Grime, 1979; Pierce et al., 2017).

57 Nonetheless, approaches based on functional traits are problematic for many reasons. First, we cannot expect traits and trait combinations to describe disturbance with sufficient accuracy because plants may respond to the same disturbance event with alternative strategies (Marks & Lechowicz, 2006). Second, disturbance consists of at least two separate dimensions, the severity of an event and its frequency (Turner, 2010), which are not easily separated by the use of plant traits. In addition, the large variety of physical processes characterizing disturbance (such as fire, wind, grazing, cutting and soil disturbance) further complicates trait-based characterization of disturbance regimes in vegetation. Finally, circular reasoning may occur in studies examining the response of plant traits to disturbance gradients when plant traits are used as proxies for disturbance, which precludes testing trait-disturbance relationships (Götzenberger, Kühn, & Klotz 2008).

70 For these reasons, Herben, Chytrý, & Klimešová (2016) proposed an alternative approach to estimate species-level disturbance indicator values independently of plant traits. Such indicator values were based on the number of species' occurrences in vegetation plots classified by severity and frequency of disturbance regimes estimated by experts for different habitat types. The indicator values reported by Herben et al. (2016) have been proved to meet
theoretical expectations for functional differentiation in plants, particularly with respect to life history categories and clonality (Herben, Klimešová, & Chytrý 2018a). However, such indicator values have been calculated only for 1,248 vascular plant species occurring in the Czech Republic and did not include specific indicator values for mowing frequency and grazing pressure – two key disturbance types affecting European vegetation of managed herbaceous ecosystems. Other expert-based indicator values have been proposed for mowing and grazing in Europe. However, they are limited in terms of the species pool and region, such as Central Russia (Ramenskii, Tsatsenkin, Chizhikov, & Antipin 1956), Germany (Briemle, Nitsche, & Nitsche 2002) and the Alps (Jouglet, 1999; Landolt et al., 2010). In addition, disturbance indicators related to the concept of hemeroby (i.e. unnaturalness of vegetation due to human impacts) have been proposed for some European plants (see e.g. Hill et al. 2002), but they do not distinguish between frequency and severity and combine other human-affected processes with disturbance, namely nutrient availability and dispersal.

Here we present a data set of species-level disturbance indicator values for 6,382 vascular plants commonly found in Europe. The large number of species and geographic extent covered by our data set can stimulate the integration of plant-disturbance relationships in the field of macroecology, functional biogeography and large-scale European vegetation monitoring and assessment.

## 2 Materials and Methods

We followed three main methodological steps to calculate disturbance indicator values (Figure 1): a) we selected vegetation plots and classified these to habitat types; b) we assigned expert-based disturbance values to the differ-
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102 ent habitat types; and c) we calculated species indicator values by averaging
103 the disturbance values of the habitat types where the species occurred. Fi-
104 nally, we examined how indicator values were distributed across different
105 main plant life forms and how they responded to functional traits and CSR
106 values (Grime, 1979; Pierce et al., 2017). All analyses were performed using
107 R version 4.1.0 (R Core Team, 2021).

2.1 Vegetation data and habitat classification

109 We based the calculation of indicator values on 1,263,388 georeferenced veg-
etation plots from the European Vegetation Archive (EVA; project 123;
110 Chytrý et al. 2016; data retrieved on 5 May 2021). The plots were mostly
111 located mostly in Europe, including a few sites in Greenland. Plots ranged
112 from 53.5°W to 62.2°E longitude and 34.8°N to 80.1°N latitude.
113

114 We used the revised version of the EUNIS (European Nature Information
115 System) Habitat Classification described by Chytrý et al. (2020) to classify
116 vegetation plots to EUNIS habitat types (hereafter, ‘habitats’). The clas-
117 sification was performed using the classification expert system EUNIS-ESy
118 (Chytrý et al. (2020); version 2021-06-01, DOI: 10.5281/zenodo.4812736).
119 This system identifies habitats based on species composition and cover-
120 abundances of particular species or species groups, accounting for the abi-
121 otic environment and geographic location as classification criteria (Chytrý
122 et al., 2020). The system evaluates each vegetation plot in terms of species
123 composition and cover and checks whether it meets the formal pre-defined
124 assignment criteria of different habitats. Some plots cannot be classified to a
125 specific habitat, either because transitional species compositions are simul-
126 taneously assigned to multiple habitats or the unusual species composition
127 prevents the classification of the plot to an existing habitat.
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to measurable changes (losses) in plant community biomass (Grime, 1979).

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In addition, we only considered disturbance of the whole community (or its whole vegetation layers) and not disturbance affecting just small patches or single individuals within the community (van der Maarel, 1993). For example, in forests, the destruction of the tree layer by stand-replacing windstorm was considered a disturbance event, but not the fall of individual dead trees. We considered both regular disturbances (e.g. annual mowing or burning of grassland, agricultural management of arable land or planned logging of a mature managed forest) and irregular disturbances (e.g. wildfires or extreme drought events).

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181 land management, drought, inundation, frost, volcanic activity and snow movement. All disturbance types are described in Table S1.1 (Appendix S1 Supporting Information).

184 Estimates of disturbance severity, grazing pressure and soil disturbance reflect the mean fraction of above-ground vegetation biomass destroyed by a single disturbance event typical of that habitat. We estimated one value for each variable and habitat type, ranging from 0 (no change in biomass) to 1 (complete loss of plant cover). Disturbance frequency and mowing frequency correspond to the estimated mean interval (in years) between two consecutive disturbance or mowing events.

191 We further considered separate values of disturbance frequency and severity estimated for the whole plant community (including all vegetation layers) and values considering only the herb layer (Herben et al., 2016). This separation was necessary to account for the fact that disturbance regimes in the tree and shrub layers differ in severity and frequency from the disturbance regimes in the herb layer of the same community. For habitats with herbaceous vegetation only, the whole-community values were equal to the herb-layer values.

199 In Appendix S1 (Supporting Information), we report additional details on the criteria used to assign disturbance values to habitats. Table S1.1 also includes the description and range of periodicity of disturbance types that were used to assign mean disturbance frequency on the habitats. The list of habitats, their disturbance values and the most important type of disturbance evaluated for each of them are reported in the Zenodo public data repository (link).
2.3 Indicator value calculation

207 Before calculating species indicator values, we stratified vegetation plots by
208 geographical location and habitat to reduce local oversampling of certain
209 vegetation types. We randomly selected one plot for each habitat that fell
210 within each cell of a grid with a resolution of 0.00225°, corresponding ap-
211 approximately to a 250 m grid. We repeated this process 999 times. Each
212 repetition resulted in a selection of 439,213 plots. Across all draws, 736,366
213 different vegetation plots were used (see Figure S2.1; Appendix S2 Support-
214 ing Information).

We followed an approach similar to Herben et al. (2016) to calculate dis-
turbance indicator values. For each repetition of vegetation plot selection,
we calculated disturbance indicator values for each species occurring in at
least 20 plots as the average of the expert-based disturbance values, weighted
by the number of plots where the species occurs in each habitat. We then
calculated the final indicator value for each species by taking the median of
the weighted mean of the disturbance values over the whole set of repeti-
tions. Finally, we excluded those species that were retained for the indicator
values calculation fewer than 10 times across the whole set of repetitions, re-
sulting in 6,382 species. We did not include cultivated plant species because
indicator values for these species were increased by disturbance in cultivated
land affecting all other species. Nevertheless, we included fruit tree species
(e.g. *Prunus domestica*) or occasionally cultivated species (e.g. *Fragaria
vesca*) because they may occur spontaneously in the vegetation.

For habitats that are never mown (207 of 236), we assigned a default value
of 100 years of mowing return time to calculate mowing frequency values
for species occurring in both mown and unmown habitats. To calculate
disturbance and mowing frequency of species, we used the inverse of return
time, which is the mean interval between successive disturbance events. We log₁₀-transformed disturbance and mowing frequency to account for their positively skewed distribution. To avoid negative values in the scale after the log-transformation, we expressed return times in centuries (i.e. years/100).

To provide a measure of uncertainty due to the 999 draws following a stratified resampling design of vegetation data from EVA, we also calculated and reported the standard deviation of the weighted mean of disturbance values. Furthermore, we explored whether plot size had an effect on indicator values and if the minimum threshold of 20 plots retained for each species is sufficient to perform the calculation. We report methodological details and results of these analyses in Appendix S3 (Supporting Information).

## 2.4 Indicator value relationship with plant characteristics

We further assessed how the indicator values were distributed across the main plant life forms described by Raunkjær (1905) (i.e. therophyte, hydroprophyte, geophyte, hemicryptophyte, chamaephyte and phanerophyte) and how they were related to the plant traits of the leaf-height-seed (LHS) scheme (Westoby, 1998), namely, specific leaf area (SLA), plant vegetative height and seed mass. These functional traits represent fundamental trade-offs of plants controlling their growth rate, competitive ability and dispersal ability. Therefore, all of these traits have been hypothesized to be involved in the response to disturbance (Laliberté et al., 2012; Vandewalle et al., 2013).

Plant life forms were compiled for a subset of 6,116 species. We carded those species for which we were not able to determine their plant life form. Functional trait data were compiled for a subset of 5,057 species in total (2,369 for SLA; 4,717 for plant height; 3,391 for seed mass) using the
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259 LEDA trait database (Kleyer et al., 2008). Missing values that could not be retrieved from LEDA were taken from TRY (Kattge et al., 2020) and other databases to obtain as much trait information as possible. Additional databases consulted for functional traits were Flora d'Italia (Pignatti, Guarino, & La Rosa 2017-2019) and the Pladias Database of the Czech Flora and Vegetation (Chytrý et al. 2021, www.pladias.cz) for plant height; D³ (Hintze et al., 2013), the Seed Information Database (Royal Botanic Gardens Kew, 2021) and PICOS (García-Gutiérrez et al., 2018) for seed mass; trait data reported by Ladouceur et al. (2019) for SLA; and BROT2.0 (Tavşanoğlu & Pausas, 2018) for both seed mass and SLA. See Table S4.1 and Table S4.2 (Appendix S4, Supporting Information) for additional details on the number of species for each plant life form and the number of trait data observations retrieved from each database, respectively.

We visually inspected box plots to explore how indicator values were distributed across different plant life forms. Based on the rationale that the positioning along the disturbance gradient affects plant functional traits of individual species, we fitted linear mixed-effect models using functional traits as dependent variables and the disturbance indicator values as predictors allowing for inclusion of a quadratic term when significantly improving the goodness of fit (i.e., the Akaike Information Criterion). We modeled potential phylogenetic dependencies by using family and genus of the species as a nested random intercept term (~ 1 | family / genus). We applied such analyses on the five main indices reported here, namely disturbance severity and frequency (both measured at the whole-community disturbance level), mowing frequency, grazing pressure and soil disturbance.

Finally, we also compared the ‘competitor, stress-tolerator, ruderal’ (CSR) scores defined by Pierce et al. (2017) to the disturbance indicator values and
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286 reported the results of these analyses in Appendix S4 (Supporting Information). We fitted linear mixed effect models to test how the C, S and R scores
287 explain variation in disturbance indicator values (Figure S4.2) and we compared indicator values grouped by main categorical strategy classes (Figure
288 S4.3). To calculate CSR values, we used a subset of 1,683 species present in
289 our data set for which SLA, leaf dry matter content (LDMC) and leaf area
290 (LA) were available from the Pladias Databasee (Chytrý et al. 2021) and
291 TRY (Kattge et al., 2020).
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3 Data structure and patterns

295

3.1 General description

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The data set contains five main independent indicator values encompassing different dimensions of disturbance for 6,382 species of the European vascular flora: disturbance severity, disturbance frequency, grazing pressure, mowing frequency and soil disturbance. In addition, the data set includes species indicator values for disturbance severity and frequency for the herb layer (Table 1). The data set includes the most frequent species of the European flora based on the frequency of their records in the EVA database, including both native European and alien plant species belonging to 166 plant families. We report figures on how the species are distributed into habitat groups and most frequent families in Appendix S5. In addition, for each species we report both the number of times the species was present in at least 20 plots of the 999 repetitions used to calculate the indicator values and the number of habitats (median) in which the species occurred in the repetitions. We also include the uncertainty (standard deviation) of each indicator value obtained across the 999 repetitions (see Figure S6.1,
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311 Appendix S6, Supporting Information). Uncertainty is higher in species occurring in a low number of habitats with contrasting disturbance regimes.
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313 The nomenclature of species and families found in the data set is based on Euro+Med PlantBase (2021).
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In general, disturbance indicator values estimate the realized disturbance
niche optimum of a species because they are based on species occurrences
in sampled vegetation. These indicator values provide information about
the ecology of the species in terms of the main characteristics of the dis-
turbance events that occur in the vegetation types where the species most
frequently occurs. For example, the highest values of disturbance severity
are found in weed flora of arable land (e.g. *Cyanus segetum*, *Ranunculus arvensis*). In contrast, the lowest values of disturbance severity are found in
many species occurring in marshes (e.g. *Carex limosa*), high-mountain cliffs
(e.g. *Campanula morettiana*, *Androsace* spp.) and some aquatic plants (e.g.
*Potamogeton* spp.). Similarly, the lowest values of mowing frequency are
found in all the species associated with various never-mown habitats, while
the highest values are found in species commonly occurring in fertile hay
meadows (e.g. *Crepis biennis*, *Schedonorus pratensis*, *Trisetum flavescens*),
which are the most frequently mown habitats.
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The indicator values presented here showed low correlation among each
other (Figure 2). This result supports the multidimensional nature of dis-
turbance as a factor and highlights the importance of using different facets
of indicator values when assessing disturbance in vegetation. However, dis-
turbance severity and frequency in the herb layer showed higher correlations
with other indicator values (Figure S6.2, see Appendix S6 Supporting In-
formation). For this reason, in this manuscript we focused on disturbance
severity and frequency at the whole-community level in this manuscript,
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338 rather than focusing on disturbance values in the herb layer.

339 **3.2 Potential for ecological research**

340 The indicator values proposed here are a tool for evaluating plant com-
munity composition and function in relation to disturbance. For example,
341 by calculating community-level means of disturbance values, our indicator
342 values could be used to explore plant taxonomic and functional diversity
343 along disturbance gradients. Furthermore, we believe that characterizing
344 species disturbance niche optima could be used to improve the effectiveness
345 of restoration and conservation strategies based on modifying disturbance
346 regimes in vegetation.
347

348 Importantly, the disturbance indicator values were determined based on
349 species occurrence in habitats. Our approach makes them independent of
350 species traits and allows us to avoid circular reasoning when focusing on the
351 response of plant functional traits to disturbance gradients. When analyzed
352 in relation to plant characteristics, we encountered clear differences in all
353 types of indicator values between plant life forms (Figure 3). For exam-
354 ple, annual plants (therophytes) had higher values of disturbance severity,
355 disturbance frequency and soil disturbance, consistent with the theoretical
356 expectation by Grime (1979) that disturbance favour more annual plants in
357 vegetation. Similarly, lower disturbance frequency for phanerophytes and,
358 to a lesser extent, geophytes, reflects that less intensive disturbance regimes
359 favour the establishment of slow-growing woody plants and those with un-
360 derground storage organs (McIntyre et al., 1995). These results are consis-
tent with the analyses of Herben et al. (2018a), but with a larger number
361 of species and a wider geographical extent. Nevertheless, both mowing fre-
362 quency and grazing pressure showed little difference among plant life forms,
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except for hydrophytes and phanerophytes, which usually grow at other sites than those managed by mowing or grazing.

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390 **4 Conclusions**

391 Quantifying disturbance niche and disturbance optimum of individual plant species is a relevant goal for ecological research, as disturbance is an important driver of plant biodiversity and function. Here we presented disturbance indicator values based on expert quantification of disturbance in different habitats. This data set extends previous work conducted for the Central European flora (Herben et al. 2016; Herben et al. 2018a) by including a larger pool of species for the whole European continent and introducing new indicator types – specifically, grazing pressure and mowing frequency.

399 While our data set focused specifically on disturbance for the European flora, we believe that our approach can be used to develop indicator values in any other system where sufficient species occurrence data and expert knowledge on habitats is available. We nevertheless emphasize that using expert estimates of habitat types to quantify species' ecological optima is based on some assumptions that must be considered when using the indicator values. In particular, we assume that disturbance regimes within the same habitat type are the same in time and space, and that species occurrence in such habitats corresponds to their optimal ecological positioning. 400
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Nevertheless, the approach presented here allows us to assess disturbance across a large number of species and, more importantly, to disentangle disturbance from other – often confounded – ecological components, such as stress, competition and productivity. 412
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For these reasons, we anticipate that the data set presented here may facilitate and stimulate the inclusion of disturbance into macroecological research. Such indicator values can be used, for instance, to test how plant morpho-physiology and functional composition of plant communities respond along disturbance gradients (Herben et al. 2018a), and can help
improve the characterization of plant functional groups for dynamic models

of vegetation and plant biodiversity (Boulangeat et al., 2012).

## 419 DATA AVAILABILITY STATEMENT

The data set is freely available in the Zenodo public data repository (https://zenodo.org/).

N.B: the data will be made freely available at the time this manuscript is ac-

cepted for publication. Since Zenodo does not support the upload

of data for double-blind revision, the data can be currently down-

loaded at the following link: https://figshare.com/s/63ea83c4d650c5fdddea)
<table><caption>Table 1: Summary of the disturbance indicator values reported in the data set.</caption><thead><tr><th>Indicator (species-level)</th><th>Description (habitat-level)</th><th>Values<br>Scale</th><th>Range</th></tr></thead><tbody><tr><td>Disturbance severity at the whole-community level</td><td rowspan="2">Mean magnitude of disturbance events (proportion of aboveground biomass killed by disturbance)</td><td rowspan="2">Proportional (0-1)</td><td>0.10 - 0.96</td></tr><tr><td>Disturbance severity in the herb layer</td><td>0.10 - 0.96</td></tr><tr><td>Disturbance frequency at the whole-community level</td><td rowspan="2">Mean frequency of disturbance events</td><td rowspan="2">log<sub>10</sub>(100/years)</td><td>0 - 2.63</td></tr><tr><td>Disturbance frequency in the herb layer</td><td>0.40 - 2.63</td></tr><tr><td>Mowing frequency</td><td>Mean frequency of mowing (i.e. cutting of plant biomass)</td><td>log<sub>10</sub>(100/years)</td><td>0 - 2.12*</td></tr><tr><td>Grazing pressure</td><td>Severity of grazing (proportion of aboveground biomass killed by grazing)</td><td>Proportional (0-1)</td><td>0 - 0.78</td></tr><tr><td>Soil disturbance</td><td>Proportional increase in cover of bare ground by furrowing or soil turning</td><td>Proportional (0-1)</td><td>0 - 0.94</td></tr></tbody></table>

* Values equal to zero correspond to 100-year return mowing time assigned by default to never-mown habitats (see Materials and Methods)
a) Vegetation data and EUNIS habitat classification

<table><thead><tr><th>plot</th><th>species</th></tr></thead><tbody><tr><td>A</td><td>Sagittaria sagittifolia</td></tr><tr><td>A</td><td>Phragmites australis</td></tr><tr><td>A</td><td>Urtica dioica</td></tr><tr><td>B</td><td>Urtica dioica</td></tr><tr><td>B</td><td>Myosotis arvensis</td></tr><tr><td>B</td><td>Cyanus segetum</td></tr><tr><td>C</td><td>...</td></tr></tbody></table>

b) Expert-based estimation of disturbance

<table><thead><tr><th rowspan="2">EUNIS Habitat</th><th colspan="5">Habitat-level disturbance values</th></tr><tr><th>Severity</th><th>Frequency (yrs)</th><th>Mowing (yrs)</th><th>Grazing</th><th>Soil</th></tr></thead><tbody><tr><td>Q51</td><td>0.2</td><td>1</td><td>10</td><td>0.1</td><td>0.1</td></tr><tr><td>V15</td><td>0.8</td><td>1</td><td>5</td><td>0.2</td><td>0.8</td></tr><tr><td>i</td><td>d<sub>ki</sub></td><td>...</td><td>...</td><td>...</td><td>...</td></tr></tbody></table>

Examples:

Q51 = Tall-helophyte bed

V15 = Bare tilled, fallow or recently abandoned arable land

c) Calculation of species indicator values

<figure><img src="image_2.png" /><figcaption>**Figure 1:** Methodological workflow to calculate disturbance indicator values including some examples for contrasting species and habitats. a) Based on vegetation-plot data from the European Vegetation Archive (EVA), we classified plots to habitat types of the European Nature Information System (EUNIS). b) We assigned disturbance values to each EUNIS habitat based on expert judgement. c) We stratified vegetation plots by geographical location and for each disturbance indicator and species, we calculated disturbance indicator values as the average of expert-based disturbance values, weighted by the number of plots where the species occurs in each habitats.</figcaption></figure>
<figure><img src="image_3.png" /><figcaption>**Figure 2:** Distributions and pairwise correlations between the main disturbance indicator values in the data set. The diagonal represents the distribution of indicator values. The top-right panels show Pearson's correlation coefficient. The bottom-left panels show density scatter plots, with lighter colors corresponding to a higher density of data points. Disturbance severity and frequency correspond to indicator values estimated at the whole-community level. See Figure S6.2 (Appendix S6 Supporting Information) for a complete correlation matrix including values for disturbance severity and frequency in the herb layer.</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>**Figure 3:** Distribution of the main disturbance indicator values across major plant life forms. Box-plots were generated for a subset of 6,116 species for which data on plant life form were available. The box represents the 50% of the central data, with the line inside corresponding to the median and the notches to the confidence interval of the median. The whiskers represent the observations within 1.5 * interquartile range values. Disturbance severity and frequency correspond to indicator values estimated at the whole-community level.</figcaption></figure>

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# SUPPORTING INFORMATION

2

## Appendix S1: Summary of expert-based assessment of disturbance in habitats

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27 Forest fire or logging is also considered when a forest regenerates in a dis-
turbed area. In contrast, ploughing of a meadow that changes it to arable
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29 land is not considered. Mire draining and peat extraction are also not consid-
ered because they change the mire to a grassland, shrubland or successional
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Defining the most important disturbance type for each habitat.

33 To assess disturbance in vegetation we considered the disturbance types re-
34 ported in Table S1.1. Most habitats are affected by varying disturbance
types characterized by different frequency and severity. For each habitat,
35 we estimated disturbance frequency and severity for those disturbance types
36 that cumulatively remove the most biomass over a long period (longer than
37 the return time of the least frequent disturbance). For example, a tem-
38 perate semi-dry grassland can be disturbed by both grazing that occurs in
39 intervals of several weeks in particular spots and extreme drought events
40 that occur irregularly once in 5 to 15 years. In this system, grazing cumu-
41 latively removes more biomass over a long period (e.g. 20 years); therefore,
42 we consider grazing as the main disturbance type and estimate the distur-
bance frequency and severity for grazing, not drought events. In contrast,
43 Mediterranean annual grasslands are occasionally grazed, but most of their
44 biomass is killed annually by the advent of the dry period in the late spring.
45 In this system, drought cumulatively removes more biomass than grazing;
46 therefore, we consider drought as the main disturbance type.
47

49 In addition to the quantification of general disturbances, we estimated
50 separately three specific types of disturbances: grazing pressure, mowing
51 frequency and soil disturbance. These disturbance types were assessed for
52 all the habitats, including those in which they are not the most important
53 habitat types.
<table><caption>**Table S1.1:** Disturbance types considered for habitat-level estimation of disturbance values and their range of periodicity. The table reports number of times each disturbance type was considered as a main disturbance type in the 236 habitats.</caption><thead><tr><td>Disturbance type</td><td>Periodicity</td><td>Notes</td><td>Number of habitats</td></tr></thead><tbody><tr><td>Wave and current action</td><td>weeks to years</td><td>Mainly extreme events in water bodies during storms.</td><td>18</td></tr><tr><td>Flooding</td><td>years to decades</td><td>Biomass removals in terrestrial or semi-aquatic environments by the kinetic energy of sudden water flow, not due to inundation.</td><td>14</td></tr><tr><td>Inundation</td><td>years to decades</td><td>Longer inundations that kill the whole plant communities by submerging vegetation (not the kinetic energy of water), e.g. filling a drained pond; excludes, for example, daily inundation in saltmarshes in coastal tidal zones.</td><td>4</td></tr><tr><td>Frost</td><td>months to decades</td><td>Frost events that kill the whole plant communities, such as in the aquatic environment; excludes regular seasonal losses of foliage or shoots in perennial herbs.</td><td>2</td></tr><tr><td>Frost-related mechanical disturbance</td><td>weeks to years</td><td>Movement due to freeze-and-thaw cycles.</td><td>1</td></tr><tr><td>Drought</td><td>years to decades</td><td>Drought period coming suddenly after a period with abundant moisture; drought kills the whole plant communities and most or all plant individuals.</td><td>5</td></tr><tr><td>Windthrow</td><td>decades to centuries</td><td>Stand-replacing events in forest vegetation. It does not include single tree falls.</td><td>8</td></tr><tr><td>Fire</td><td>years to decades</td><td></td><td>44</td></tr><tr><td>Volcanic activity</td><td>months to centuries</td><td></td><td>3</td></tr><tr><td>Substrate movement</td><td>weeks to years</td><td>Includes both gravitation-driven movements (such as on screens) and wind-driven movements (such as in sand dunes)</td><td>19</td></tr><tr><td>Erosion</td><td>days to millenia</td><td>Erosion of substrate (e.g. on cliffs and steep slopes)</td><td>17</td></tr><tr><td>Snow movement</td><td>years to decades</td><td>Avalanches and creeping snow movement on slopes</td><td>2</td></tr><tr><td>Logging</td><td>decades</td><td></td><td>40</td></tr><tr><td>Mowing</td><td>months to years</td><td>Including cutting of dwarf-shrub vegetation</td><td>9</td></tr><tr><td>Grazing</td><td>weeks to years</td><td>Including trampling by grazing animals</td><td>118</td></tr><tr><td>Trampling (humans)</td><td>days to weeks</td><td>Trampling by humans. Includes the movement of vehicles.</td><td>14</td></tr></tbody></table>
<table><tr><td>Vegetation removal by humans in settlements, around buildings and infrastructures</td><td>weeks to months</td><td>Including cuttings and herbicide application</td><td>18</td></tr><tr><td>Arable land management</td><td>months</td><td>Including tillage, herbicide application, weeding, hoeing</td><td>5</td></tr><tr><td>Pathogen outbreaks</td><td>decades</td><td>Outbreaks of pathogenic organisms (e.g. insect herbivores, fungi and bacteria)</td><td>11</td></tr></table>

54 • **Grazing pressure** included grazing by large herbivores, both domestic and wild, mainly mammals, but in saltmarshes, aquatic habitats and wetlands also by birds. Grazing by invertebrates and small vertebrate herbivores was not considered. Grazing pressure was estimated on a scale from 0 to 1, where 0 means that the habitat is not grazed and 1 means that all the vegetation is removed by grazing at least once a year.

61 • **Mowing return time (frequency)** is the time between two consecutive mowings *

63 • **Soil disturbance includes** mechanical disturbance of the soil surface or the upper soil layer by furrowing or soil turning. It is estimated on a scale from 0 to 1, where 0 means no soil disturbance and 1 means soil turning across the whole area of the habitat, such as ploughing on arable land.

68 * = For the habitats that are never mown, a value of 100 years was used for the calculation of species indicator values. See Materials and Methods of the main article.
71 REFERENCES

72 Grime, J. (1979). *Plant strategies and vegetation processes*. Chichester

73 New York: Wiley.

74 van der Maarel, E. (1993). Some remarks on disturbance and its relations to diversity and stability. *Journal of Vegetation Science*, 4(6), 733-736.

76 White, P. S., & Jentsch, A. (2001). The search for generality in studies of disturbance and ecosystem dynamics. *Progress in Botany*, 62, 399-450.

78 Springer, Berlin, Heidelberg.
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<figure><img src="image_5.png" /><figcaption>**Figure S1.1:** Density plots (diagonal), pairwise correlations (top-right) and scatter plots (bottom-left) for the disturbance values assigned to the 236 habitats. DSW = disturbance severity (at the whole community level); DSH = disturbance severity in the herb layer; DRTW = disturbance return time (at the whole community level); DRTH = disturbance return time in the herb layer; MRT = mowing return time; GP = grazing pressure; SD = soil disturbance. Return time for disturbance and mowing is expressed in years. A 100-year value was assigned by default to MRT for never mown habitats.</figcaption></figure>
Appendix S2: Geographic distribution of vegetation data

<figure><img src="image_6.png" /><figcaption>**Figure S2.1:** Geographic distribution of the density (on a natural logarithm scale) of vegetation plots from the European Vegetation Archive (EVA) used to calculate disturbance indicator values (736,366 in total, after 999 random stratification rounds).</figcaption></figure>
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## Appendix S3: Sensitivity analyses

In this appendix we describe the methods and results of sensitivity analyses exploring the role of plot size in affecting indicator values and test the minimum plot number to calculate the indicator values.

First, to test the role of plot size on the indicator values, we subset our vegetation plot data by excluding plots with missing information on plot size and stratified the data set into three categories based on plot sizes ranges (i.e., 'small', 'medium', 'large'), resulting in a total of 544,433 plots. The assignment to the three categories depended upon the vegetation type (see Table S3.1). Then, we recalculated the indicator values for each plot size range category and compared the results to our original values.

<table><caption>**Table S3.1:** Ranges of plot size for each category (= 'Small', 'Medium' and 'Large'). Range values were assigned differently to forest or non-forest vegetation. The table report the total percentage for each category over 544,433 plots.</caption><thead><tr><th rowspan="2">Plot size category</th><th colspan="2">Plot size range (m²)</th><th rowspan="2">% of plots</th></tr><tr><th>Forest</th><th>Non-forest</th></tr></thead><tbody><tr><td>'Small'</td><td>1 - 50</td><td>1 - 4</td><td>29.9</td></tr><tr><td>'Medium'</td><td>51 - 100</td><td>5 - 81</td><td>50.2</td></tr><tr><td>'Large'</td><td>101 - 1000</td><td>82 - 1000</td><td>19.9</td></tr></tbody></table>

We argue that plot size cannot affect directly disturbance indicators because expert-based disturbance values of habitat types were assigned independently from vegetation plot characteristics. Indeed, we show that patterns are consistent to our original indicator values (Pearson's r ≥ 0.84) (see Figure S3.1 and Figure S3.2). However, the values of disturbance indicators can slightly vary depending on plot size because the latter depend upon habitat types, from which disturbance values are derived.
<figure><img src="image_7.png" /><figcaption>**Figure S3.1:** Relationship between our original indicator values (y-axis) and indicator values re-calculated for each subset of plot size category (='Small', 'Medium', 'Large') (x-axis) for disturbance severity and frequency (at the whole community level) and disturbance severity and frequency in the herb layer. The panels include the Pearson's correlation (r) and number of observations.</figcaption></figure>
<figure><img src="image_8.png" /></figure>

**Figure S3.2:** Relationship between our original indicator values (y-axis) and indicator values re-calculated for each subset of plot size category (='Small', 'Medium', 'Large') (x-axis) for Mowing frequency, Grazing pressure and Soil disturbance. The panels include the Pearson's correlation (r) and number of observations.
Second, to test that a minimum of 20 plots per species is sufficient for estimating disturbance indicator values, we report the results of a sensitivity analysis in which we recalculated the indicator values by randomly selecting, where possible, 20 plots only for each species in each of the 999 repetitions in which we calculated the disturbance indicator values. We show that the values obtained this way are nearly the same to disturbance indicator values reported in our main manuscript (Pearson's *r* ≥ 0.99) (Figure S3.3), demonstrating that a minimum of 20 plots per species represent a robust threshold for calculating disturbance indicator values.

<figure><img src="image_9.png" /><figcaption>**Figure S3.3:** Relationship between our original indicator values (y-axis) and indicator values re-calculated by randomly selecting 20 plots for each species in each round (x-axis) across the 6,382 species. Pearson's correlation (r) was higher than 0.99 across all the pairwise comparisons.</figcaption></figure>
## 107 Appendix S4: Plant functional traits and C-S-R data relationships with disturbance indicator values

<table><caption>**Table S4.1:** Number of species (observations) available for each plant life form. The table reports both counts including those species that fall into more plant life form categories (data used for Figure 3 in the main manuscript), as well as the number of species for each category excluding the species falling into more than one plant life form category.</caption><thead><tr><th rowspan="2">Plant life form</th><th colspan="2">Species number (observations)</th></tr><tr><th>Multiple plant life form</th><th>Unique plant life form</th></tr></thead><tbody><tr><td>Phanerophyte</td><td>546</td><td>508</td></tr><tr><td>Chamaephyte</td><td>656</td><td>437</td></tr><tr><td>Hemicryptophyte</td><td>3584</td><td>2902</td></tr><tr><td>Geophyte</td><td>700</td><td>364</td></tr><tr><td>Hydrophyte</td><td>120</td><td>89</td></tr><tr><td>Therophyte</td><td>1272</td><td>1059</td></tr></tbody></table>

<table><caption>**Table S4.2:** Number of species-level observations (trait data) retrieved from each database for each plant functional trait.</caption><thead><tr><th>Database name</th><th>Plant height</th><th>Seed mass</th><th>SLA</th><th>Source</th></tr></thead><tbody><tr><td>BROT2.0</td><td>-</td><td>361</td><td>305</td><td>Tavşanoğlu & Pausas (2018)</td></tr><tr><td>D<sup>3</sup></td><td>-</td><td>169</td><td>-</td><td>Hintze et al. (2013)</td></tr><tr><td>Flora d'Italia</td><td>2304</td><td>-</td><td>-</td><td>Pignatti et al. (2017-2019)</td></tr><tr><td>SID-KEW</td><td>-</td><td>89</td><td>-</td><td>Royal Botanic Gardens Kew (2021)</td></tr><tr><td>Ladouceur</td><td>-</td><td>-</td><td>48</td><td>Ladouceur et al. (2019)</td></tr><tr><td>LEDA</td><td>2149</td><td>244</td><td>1803</td><td>Kleyer et al. (2008)</td></tr><tr><td>PICOS</td><td>-</td><td>50</td><td>-</td><td>García-Gutiérrez et al. (2018)</td></tr><tr><td>PLADIAS</td><td>122</td><td>-</td><td>-</td><td>Chytrý et al. (2021)</td></tr><tr><td>TRY</td><td>142</td><td>678</td><td>213</td><td>Kattge et al. (2020)</td></tr></tbody></table>

## 109 References

110 Chytrý M., Danihelka J., Kaplan Z., Wild J., Holubová D., Novotný P., .

111 . . & Pyšek P. (2021) Pladias Database of the Czech Flora and Vegetation.
112 – *Preslia* 93(1), 1–87.

113 García-Gutiérrez, T., Jiménez-Alfaro, B., Fernández-Pascual, E., & Müller, J. V. (2018). Functional diversity and ecological requirements of alpine vegetation types in a biogeographical transition zone. *Phytocoenologia, 48*(1), 77-89.

117 Hintze, C., Heydel, F., Hoppe, C., Cunze, S., König, A., & Tackenberg, O. (2013). D3: the dispersal and diaspore database–baseline data and statistics on seed dispersal. *Perspectives in Plant Ecology, Evolution and Systematics, 15*(3), 180-192.

121 Kattge, J., Bönisch, G., Díaz, S., Lavorel, S., Prentice, I. C., Leadley, P., ... & Cuntz, M. (2020). TRY plant trait database–enhanced coverage and open access. *Global Change Biology, 26*(1), 119-188.

124 Kleyer, M., Bekker, R. M., Knevel, I. C., Bakker, J. P., Thompson, K., Sonnenschein, M., ... & Peco, B. (2008). The LEDA Traitbase: a database of life-history traits of the Northwest European flora. *Journal of Ecology, 96*(6), 1266-1274.

128 Ladouceur, E., Bonomi, C., Bruelheide, H., Klimešová, J., Burrascano, S., Poschlod, P., ... & Jiménez-Alfaro, B. (2019). The functional trait spectrum of European temperate grasslands. *Journal of Vegetation Science, 30*(5), 777-788.

132 Pignatti, S., Guarino, R., & La Rosa, M. (2017-2019). Flora d'Italia. Edagricole: Bologna, Italy.

134 Royal Botanic Gardens Kew. (2021). Seed information database (sid). version 7.1. Retrieved from http://data.kew.org/sid/ (August 2021)
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136 Tavşanoğlu, Ç., & Pausas, J. G. (2018). A functional trait database for Mediterranean Basin plants. *Scientific Data, 5*(1), 1-18

137
<figure><img src="image_10.png" /></figure>

**Figure S4.1:** Relationship between plant functional traits (y-axes) and main disturbance indicator values (x-axes) across individual species. Lighter colors of the bins correspond to a higher density of data points. The panels include the predicted line of the linear mixed-effect model and related marginal R². The line is fitted from a quadratic relationship if this significantly improved the goodness of fit (= Akaike Information Criterion, AIC). Plant functional trait values are on a log scale.
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<figure><img src="image_11.png" /><figcaption>**Figure S4.2:** Relationship between competitor (C), stress-tolerator (S) and ruderal (R) Grime's scores (x-axes) and main disturbance indicator values (y-axes) across individual species for a subset of 1,683 species. Lighter colors of the bins correspond to a higher density of data points. The panels include the predicted line of the linear mixed-effect model and related marginal R².</figcaption></figure>
<figure><img src="image_12.png" /></figure>

**Figure S4.3:** Distribution of the main disturbance indicator values across the main Grimes' plant strategy categories (C = competitor; S = stress-tolerator; R = ruderal). The box plots are obtained on a subset of 1,683 species for which data on C-S-R scores where available. The colors of the box represent the positioning of each category in the color wheel of the CSR triangle by Pierce et al. (2017). The box represents the 50% of the central data, with the line inside corresponding to the median and the notches to the confidence interval of the median. The whiskers represent the observations within 1.5 * interquartile range values.
<figure><img src="image_13.png" /><figcaption>Appendix S5: Number of species per plant family and habitat groups</figcaption></figure>

**Figure S5.1:** Number of species included in the data set for each plant family. Only the 30 most frequent families are shown.
<figure><img src="image_14.png" /><figcaption>b)</figcaption></figure>

<figure><img src="image_15.png" /></figure>

**Figure S5.2:** Number of species included in the data set found in different EUNIS habitat groups (see Chytrý et al., 2020) based on 736,366 EVA plots. Panel a) displays species found in at least one plot belonging to a given habitat group. Panel b) displays species found in at least 20 plots belonging to a given habitat group.
Appendix S6: Distribution of uncertainty and pairwise correlation matrix of indicator values

<figure><img src="image_16.png" /><figcaption>b)</figcaption></figure>

<figure><img src="image_17.png" /></figure>

**Figure S6.1:** *a)* Distribution of uncertainty for the whole set of indicator values available in the data. Uncertainty is calculated as the standard deviation of mean indicator values across the 999 draws of randomly sampled vegetation data. *b)* Relationship between the standard deviation values and sample size, namely, the mean number of habitats where the species is found across the 999 draws. Higher values of standard deviation are found in those species occurring in low number of habitats with contrasting disturbance regimes.
<figure><img src="image_18.png" /></figure>

**Figure S6.2:** Pairwise correlations (top-right) and density scatter plots (bottom-left) for the whole set of indicator values available in the data. Lighter colors in the bottom-left panels correspond to a higher density of data points. The first five indicators starting from top left (DSW, DFW, MF, GP and SD), correspond to the main indicators presented in the main manuscript. DSW = disturbance severity (at the whole community level); DSH = disturbance severity in the herb layer; DFW = disturbance frequency (at the whole community level); DFH = disturbance frequency in the herb layer; MF = mowing frequency; GP = grazing pressure; SD = soil disturbance.
a) Vegetation data and EUNIS habitat classification
Page 57 of 59
b) Expert-based estimation of disturbance
Global Ecology and Biogeography

<table><thead><tr><th>plot</th><th>species</th></tr></thead><tbody><tr><td>A</td><td>Sagittaria sagittifolia</td></tr><tr><td>A</td><td>Phragmites australis</td></tr><tr><td>A</td><td>Urtica dioica</td></tr><tr><td>B</td><td>Urtica dioica</td></tr><tr><td>B</td><td>Myosotis arvensis</td></tr><tr><td>B</td><td>Cyanus segetum</td></tr><tr><td>C</td><td>...</td></tr></tbody></table>

<table><thead><tr><th colspan="5">Habitat-level disturbance values</th></tr><tr><th>EUNIS Habitat</th><th>Severity</th><th>Frequency (yrs)</th><th>Mowing (yrs)</th><th>Grazing</th><th>Soil</th></tr></thead><tbody><tr><td>Q51</td><td>0.2</td><td>1</td><td>10</td><td>0.1</td><td>0.1</td></tr><tr><td>V15</td><td>0.8</td><td>1</td><td>5</td><td>0.2</td><td>0.8</td></tr><tr><td>i</td><td>d<sub>ki</sub></td><td>...</td><td>...</td><td>...</td><td>...</td></tr></tbody></table>

<table><thead><tr><th>plot</th><th>EUNIS habitat</th></tr></thead><tbody><tr><td>A</td><td>Q51</td></tr><tr><td>B</td><td>V15</td></tr><tr><td>C</td><td>...</td></tr></tbody></table>

<figure><img src="image_19.png" /></figure>

<figure><img src="image_20.png" /></figure>

Examples:
Q51 = Tall-helophyte bed
V15 = Bare tilled, fallow or recently abandoned arable land

b) Calculation of species indicator values

<figure><img src="image_21.png" /></figure>

<figure><img src="image_22.png" /></figure>

for indicator k; species j:

$$D_{kj} = \frac{\sum_{i=1}^{n} w_{ji} d_{ki}}{\sum_{i=1}^{n} w_{ji}}$$

$d_{ki}$ = Disturbance value for disturbance type k in habitat i

$w_{ji}$ = Number of occurrences of species j in habitat i
Disturbance
severity

<figure><img src="image_23.png" /></figure>

$\rho = -0.34$

Global Ecology and Biogeography

$\rho = 0.19$

$\rho = 0.07$

Page 58 of 59

$\rho = 0.48$

Disturbance
frequency

<figure><img src="image_24.png" /></figure>

$\rho = 0.48$

$\rho = 0.2$

$\rho = 0.43$

Mowing
frequency

<figure><img src="image_25.png" /></figure>

$\rho = 0.22$

$\rho = 0.43$

Grazing
pressure

<figure><img src="image_26.png" /></figure>

$\rho = -0.02$

<figure><img src="image_27.png" /></figure>

Soil
disturbance

<figure><img src="image_28.png" /></figure>

<figure><img src="image_29.png" /></figure>

<figure><img src="image_30.png" /></figure>

<figure><img src="image_31.png" /></figure>
<figure><img src="image_32.png" /></figure>