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Aims Ellenberg-type indicator values are expert-based rankings of plant species according to their ecological optima on main environmental gradients. Here we extend the indicator-value system proposed by Heinz Ellenberg and co-authors for Central Europe by incorporating other systems of Ellenberg-type indicator values (i.e., those using scales compatible with Ellenberg values) developed for other European regions. Our aim is to create a harmonized dataset of Ellenberg-type indicator values applicable at the European scale. Methods We collected European datasets of indicator values for vascular plants and selected 13 datasets that used the nine-, ten- or twelve-degree scales defined by Ellenberg for light, temperature, moisture, reaction, nutrients and salinity. We compared these values with the original Ellenberg values and used those that showed consistent trends in regression slope and coefficient of determination. We calculated the average value for each combination of species and indicator values from these datasets. Based on species co-occurrences in European vegetation plots, we also calculated new values for species that were not assigned an indicator value. Results We provide a new dataset of Ellenberg-type indicator values for 8,908 European vascular plant species (8,168 for light, 7,400 for temperature, 8,030 for moisture, 7,282 for reaction, 7,193 for nutrients, and 7,507 for salinity), of which 398 species have been newly assigned to at least one indicator value. Conclusions The newly introduced indicator values are compatible with the original Ellenberg values. They can be used for large-scale studies of the European flora and vegetation or for gap-filling in regional datasets. The European indicator values and the original and taxonomically harmonized regional datasets of Ellenberg-type indicator values are available in Supplementary Information and the Zenodo repository.
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J Veg Sci. 2023;34:e13168. 
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Journal of Vegetation Science
Ellenberg- type indicator values for European vascular plant
Lubomír Tichý1| Irena Axmanová1|rgen Dengler2,3,4 | Riccardo Guarino5|
Florian Jansen6| Gabriele Midolo1| Michael P. Nobis7| Koenraad Van Meerbeek8,9 |
SvetlanaAćić10 | Fabio Attorre11 | Erwin Bergmeier12 | Idoia Biurrun13 |
Gianmaria Bonari14 | Helge Bruelheide4,15 | Juan Antonio Campos13 |
AndražČarni16,17 | Alessandro Chiarucci18 |MirjanaĆuk19 |RenataĆušterevska20 |
Yakiv Didukh21 |DanielDítě22 |ZuzanaDítě22 | Tetiana Dziuba21 |
Giuliano Fanelli11 | Eduardo Fernández- Pascual23 | Emmanuel Garbolino24 |
Rosario G. Gavilán25 | Jean- Claude Gégout26 | Ulrich Graf7| Behlül Güler27 |
Michal Hájek1| Stephan M. Hennekens28 | Ute Jandt4,15 |AnniJašková1|
Borja Jiménez- Alfaro23 | Philippe Julve29| Stephan Kambach15 |
Dirk Nikolaus Karger7| Gerhard Karrer30 |AliKavgacı31 | Ilona Knollová1|
Anna Kuzemko1,21 |FilipKüzmič16 | Flavia Landucci1| Attila Lengyel32 |
Jonathan Lenoir33 | Corrado Marcenò34 | Jesper Erenskjold Moeslund35 |
Pavel Novák1| Aaron Pérez- Haase36 |TomášPeterka1| Remigiusz Pielech37, 3 8 |
Alessandro Pignatti11|ValerijusRašomavičius39 |SolvitaRūsiņa40 |
Arne Saatkamp41,42 | Urban Šilc16 |ŽeljkoŠkvorc43 | Jean- Paul Theurillat44,45 |
Thomas Wohlgemuth7| Milan Chytrý1
10DepartmentofBotany,FacultyofA griculture,UniversityofBelgrade,Beograd,Serbia
11Depar tmentofEnvironmentalBiolog y,SapienzaUniversityofRome,Roma,Italy
12VegetationEcology&PlantDiversit y,AlbrechtvonHallerInstituteofPlantSciences,UniversityofGöt tingen,Göttingen,Germany
13Depar tmentofPlantBiologyandEcology,UniversityoftheBasqueCountryUPV/EHU,Bilbao,Spain
14FreeUniversit yofBozen-Bolzano,Bolzano,Italy
16ResearchCentreoftheSlovenianAc ademyofSciencesandAr ts,JovanHadžiInstituteofBiology,Ljubljana,Slovenia
©2022TheAuthors.Journal of Vegetation SciencepublishedbyJohnWiley&SonsLtdonbehalfofInternationalAssociationforVegetationScience.
Journal of Vegetation Science TICHÝ et al.
18BIOMEL ab,DepartmentofBiological,Geological&EnvironmentalSciences,AlmaMaterStudiorum-UniversityofBologna,Bologna,Italy
19Depar tmentofBiologyandEcology,FacultyofScience,UniversityofNoviSad,NoviSad,Serbia
20FacultyofNaturalSciencesandMathematics,Ss.CyrilandMethodiusUniversit y,Skopje,NorthMacedonia
26UniversitédeLorraine,AgroParisTech,INR AE,UMRSilva,Nancy,France
27Biolog yEducation,DokuzEylülUniversity,Buca,Turkey
30DepartmentofIntegrativeBiologyandBiodiversityResearch,Universit yofNaturalResourcesandLifeSciencesVienna,Vienna,Austria
36DepartmentofEvolutionaryBiology,EcologyandEnvironmentalSciences,Universit yofBarcelona,Barcelona,Spain
37Depar tmentofForestBiodiversity,FacultyofForestr y,UniversityofAgricultureinKraków,Kraków,Poland
40FacultyofGeographyandEarthSciences,UniversityofLatvia,Riga,Lat via
41Conser vatoireBotaniqueNationalMéditerranéen,Hyères,France
43UniversityofZagreb,FacultyofForestr yandWoodTechnology,Zagreb,Croatia
44FondationJ.-M.Aubert,Champex-Lac,Swit zerland
45Depar tmentofPlantSciences,UniversityofGeneva,Chambésy,Switzerland
Funding information
Co- ordinating Editor:MeelisPärtel
Aims: Ellenberg-typeindicatorvalues areexpert-basedrankingsofplantspeciesac-
theindicator-valuesystem proposed by HeinzEllenbergandco-authorsforCentral
ble at the European scale.
Methods: WecollectedEuropeandatasetsofindicatorvaluesforvascularplantsand
selected13datasetsthat used the nine-, ten- or twelve-degree scales defined by
Ellenberg forlight,temperature,moisture,reaction,nutrientsand salinity.Wecom-
pared these values with the original Ellenberg values and used those that showed
theaverage value for eachcombination of species andindicatorvaluesfrom these
data sets. Based onspecies’ co-occurrences in European vegetation plots,we also
Results: We provide a new data set of Ellenberg-type indicator values for 8908
European vascular plant species (8168 for light, 7400 for temperature, 8030 for
Journal of Vegetation Science
TICHÝ et al.
Bioindication of abiotic site conditions from environmental re-
lationships of plant species has a long tradition (Cajander, 1926;
Iversen,1936). Seminal workwas done by theGerman vegetation
ecologist Heinz Ellenberg, who published a comprehensive data
setof indicator values forvascularplant species (Ellenberg, 1974).
surements,mainlyfromGermany.Ellenberg defined indicator val-
ues for seve n abiotic environme ntal variables : light, temperat ure,
continent ality, moisture , soil reaction , nutrient (nitroge n) content,
and salin ity. While the fir st two varia bles relate main ly to above-
ground con ditions, the l ast four descr ibe substrate (s oil or water)
soilfertility,suchasthecombined availabilityof both nitrogenand
phosphorus(Boller-Elmer,1977;Briemle,1986;Hill&Carey,1997 ).
Therefore, Ellenberg's original nitrogen values arenowadaysmore
oftencallednutrient values(Ellenbergetal.,1992),whilethere are
attemptstodevelopseparate indicatorvalues for these two nutri-
Ellenbe rg indicator va lues were defin ed on ordinal sc ales that
characterize the relativepositionofthecentroid of aspecies'real-
izedone-dimensional niche related totherespective environmen-
optimum towards the lower end of the environmental gradient,
whereas a high value corresponds to the position at the higher end.
For exampl e, low values of th e light scale are as signed to shade-
tolerantspecies, whereas high values are assigned tospecies that
Ellenberg's system was inspired in part by the ideas of
Cajander(1926), who used associationsofplant species to eval-
uate fores t types and pr oductivit y, and Iver sen (1936), who a r-
ranged plants into response groups to environmental variables
relevant to plantgrowth.However,Ellenberg(1948,1950,1952)
was the first touse numericalcodes instead ofverballydefined
levels of environmental gradients. Ellenberg (1948) also pro-
posed using these codes to calculate community means based on
species presence and community-weightedmeansbased on spe-
et al., 1967; Zlatník et al., 1970)adoptedEllenberg'sconcept of
otherpartsofEurope.Notonlyvascularplantsbutlateralsobr yo -
developed to indicate disturbance (Briemle & Ellenberg, 1994;
Repeatedly updated and refined, Ellenberg indicator values
used tool for rapidly estimating environmental conditions without
direct measurements (Diekmann, 2003; Holtland et al., 2010). In
the Web ofSciencedatabase, 907articles with the keywords (in-
cluding wo rds used in abstr acts) ‘Ellen berg’ AND ‘Ind icator’ were
register ed between 1 J anuary 1974 and 30 June 20 22, indicat ing
their importance to plant ecologists.Severalstudiesfoundagood
calcul ated from Ellenbe rg indicator valu es and values of env iron-
mental variables measured in situ(Ellenbergetal.,1992;Herzberger
& Karrer, 1992; Hil l & Carey, 1997; Ert sen et al., 1998; Sch affers
& Sýkora, 2000; Wamelink et a l., 2002; Diekman n, 2003; Chytr ý
et al., 2009; Sicuriel lo et al., 2014). S ome authors also d iscussed
the consistency of indicator values between different geograph-
ical areas (Diekmann &Lawesson, 1999;Gégout& Krizova, 2003;
Godefroid & Dana, 2007;Wasof et al., 2013).BecauseEllenberg's
original datasetfocusedon plantsoccurringinthewesternpartof
CentralEurope, other authors proposedindicator values for other
missing from Ellenberg'soriginal dataset andoftencontained dif-
etal., 2002;Zarzycki et al.,2002; Hilletal., 2004; Pignatti, 2005;
Landolt et al., 2010; Didukh, 2011; Chytrý et al., 2018; Domina
etal.,2018;Guarino& LaRosa, 2019; Jiménez-Alfaro etal.,2021;
species have been newly assigned to at least one indicator value.
Conclusions: Thenewlyintroducedindicatorvaluesarecompatiblewiththeoriginal
theoriginalandtaxonomically harmonizedregionaldatasetsofEllenberg-typeindi-
Journal of Vegetation Science TICHÝ et al.
alsocreated(e.g.Hájeketal.,2020—mi res;Dítěetal.,2023 saline
on European vegetation,catalyzed bythe launch of the European
databas e of vegetation plot s (European Vegetati on Archive, EVA;
Chytrýet al., 2016), requireacoherentsystemofspecies-levelin-
dicator values. Althoughregionalsystemsof indicator values have
set and the methods used to compile it.
Wecompiledadatabaseof 13publishedEuropeandatasets ofin-
dicator valuesforvascularplant speciesdefinedon the samenine-
degree sc ale (or 10-deg ree scale for sali nity and 12-de gree scale
for moist ure) as the original Ell enberg indicat or values (Ellenbe rg
etal.,1992,2001).WerefertothesedatasetsasEllenberg- type in-
dicator values. Datasetswith scales containing alower numberof
moisture),we acceptedit, provided that:(1)the additionaldegrees
other degrees retained the same meaning as in the original Ellenberg
definedfor CentralEurope to12degreesto reflectMediterranean
conditions; Pignatti, 2005) or (2) the additional degrees repre-
sented intermediate values on the nine- or 12-degree scale (e.g.
in Didukh , 2011). We consid ered only dat a sets based en tirely or
calculatedfrom vegetation plots withoutexpert-basedassessment
ofvaluesfor individualspecies (e.g. Lawessonet al.,2003 forthe
The13indicator-valuedata setsthat met theaboveconditions
andthe Alps(Landolt et al.,2010;temperature values only,as the
Ukraine (Didukh, 2011; only the light, temperature and moisture
values , as the others c annot be matche d to the Ellenber g scales);
Italy (Guarino &La Rosa,2019,acorrected versionpreparedbyR.
Guarino for this study); South Aegean r egion of Greece (Böhling
10degrees for salinity,andninedegreesfor the othervalues.The
first a uthor of the orig inal data set. T herefore, we inte grated the
data set s using 12-deg ree scales fo r temperatur e and moisture , a
andnutrients. Wedid not includethe Swedishindicatorvalues for
We omitted the indicator values for continentality because
theyarebased onspecies’geographical ranges.Continentalityval-
ues may have an ambiguous meaning at the local scale since they
in temperature and precipitation, diurnal differences in tempera-
ture, annual minimum temperatures, and drought. Moreover, Berg
et al. (2017) id entified meth odological we aknesses in th e original
Ellenbergapproachtocontinentality values,proposedanimproved
protocol for their compilation, and defined new for mally-verified
taxaacrossthe 13datasets accordingtotheEuro+MedPlantBase
(http://europ We merged subspecies, varieties and
all,suchasspeciesoftheAchillea millefolium group in the A. millefo-
gates defined in somedata setson the national or regional scales,
these aggregates are valid at the European scale. Forinfraspecific
andaggregate. Somedatabases provided indicator values for both
individualspeciesandaggregates.Although some oftheseaggre-
gates are notregularly usedin vegetation science, have aregional
The new sy stem of indicat or values was pr epared by cal culat-
ing the ari thmetic mean for e ach combination of species and i n-
dicator value across all compatible regional data sets in which an
indicatorvaluewas defined for the target species.As afirststep,
wetestedwhethertheindicatorvaluesofeach ofthe12datasets
(other than t he original Ellen berg data set) we re compatible wit h
theEllenbergvalues.Weconductedtwocomparisons. Forthefirst
one,wetestedadirectpairwise relationship betweentheoriginal
(independent variable) andvaluesfor the same species in a differ-
Journal of Vegetation Science
TICHÝ et al.
second comparison, we used vegetation plots from the EVAdata-
vegetatio n types sa mpled acros s Europe were use d. The territo ry
ofRussian Federation, Georgia, Armenia,and Azerbaijanwere not
include d due to their perip heral biogeogra phical locatio n, lack of
Weselected onlyvegetation plotsthatcontainedatleastfive spe-
for light indicator values, 413,832 for temperature, 615,301 for
moisture,490,617forreaction, 575,406fornutrients and673,141
Based on the regression analyses described above, we se-
lected data sets that showed consistent trends in both the direct
nation (R2) an d slope. The coe fficient of d eterminatio n shows the
amountofvariationinthedependentvariable explainedbythere-
gression.However,thesameR2ca nbeobtainedwith vas t lydiffer ent
slopes. Therefore, we alsoused slope, which mainlyindicates dif-
ontheempirical assessment of theregressionresults, we selected
rangefrom 0.5to 1.2andR2washigherthan 0.5.Theonlyexcep-
tionwasthesalinitydatasetforCentralEurope(Dítě etal.,2023),
which, incontrast to Ellenbergsalinity values, did not include any
into the same species or aggregate was more than three indicator
value units across all datasets, and the range crossed the central
value(i.e.avalueof 5forthenine-degreescales,avalueof4.5for
the 10-degree s alinity sc ale and a value of 6 .5 for the 12-de gree
scales), we r eported no i ndicator valu e. The conditi on of crossing
either the averaging or median calculation that had more than one
decimal place were rounded to one decimal place.
To assign indicator values to species for which indicator val-
ues were notavailablein any ofthe datasetsbut whichoccurred
byChytrý etal. (2018). First, for each ofthesetargetspecies, we
searchedfortheset ofother species thathadthemost similaroc-
currence pattern across EVA plots. We measured the degree of
we listed all species with an indicator value that had a similar oc-
currencepattern(interspecificassociationofphi> 0.1).Iftherewere
atleastfive suchspecies, we calculated themean(roundedto one
with the highest phivalue.Ift her ewerefewertha nfi vesuchsp ecies,
no new indicator values were calculated.
Mean indi cator value s always have a narrowe r range than th e
the compatibility between the newly calculated and original indica-
torvalues.Tostandardizethe rangeof indicator valuesforspecies
sets. Fo r a set of these spe cies, we calcul ated a linear regr ession
dent variable) andaverageindicator values fromthe regional data
ues were not previously available.
Any subjective adjustment of indicator values was avoided.
However, indicator values for obligatory epiphytic hemiparasites
germinatingontrees(Arceuthobium, Loranthus and Viscum)werenot
includedinthef inallistinthecaseofnutrient s,re ac tionandsalinity.
Wetestedthe validity of the harmonizedEuropean datasetof
indicatorvalues usingan example of indicator values for tempera-
peraturedata.We calculated the unweighted community mean of
temperature indicator values across species ineach EVAplot that
contained at least five species (413,832 plots) and related them
to modelled mean summer temperatures from the Chelsa data-
base (Kar ger et al., 2017; bio10 — daily me an air temper atures of
the warmest quarter for the period of 1981–2010). Data process-
ing and analyses were performedusing the programs JUICE v.7.1
Of the 12 Ellenberg-type indicator-value data sets (i.e., exclud-
ing the ori ginal Ellenber g data set), 11 were found to b e at least
partially compatible with the originalEllenberg data set (Table 1,
AppendixS1) afterbeing testedwithspecies-basedregressionand
plot-based regression(Appendix S2). Outlier datasets thatdidnot
ses. Indicator values forthe Cantabrian Mountainswere excluded
tor values for moistureand salinity but excluded the other values
Journal of Vegetation Science TICHÝ et al.
indicat or values for ligh t and moisture , but excluded tem perature
vironmentalvariables were defined for 5,398species.Atleastone
regional dataset.Thematrixofcorrelationsbetweenindicatorval-
ues and frequency histograms forindividual indicator values, both
for species and community means calculated for EVA plots, are
shown in Figures 2 and 3.
The set of 1,79 0,582 vegetati on plots fro m the EVA databas e
nomencl ature. Of these, 7,918 (70.9%) had at leas t one indicator
valuewasavailable for99.7%ofallspeciesoccurrencesintheEVA
vegetation plots.
Linearregressionsbetweencommunity-meanvalues forEVA
vegetatio n plots calcul ated from the new dat a set of European
ture from the Chelsa data set showed a stronger relationship
(R2 = 0.49)tha nregressions cal culatedfrom each regional data
values showed negligible differences in slope and coefficient of
determination when calcu lated with or withou t the species for
which the indicator value had beenderivedfrom the EVA-based
composition undernatural conditions. This dataset covers a large
etat ion.A lth oughitd oesnoti nclud ealltheEu ropeanspecies ,itcon-
tainsmost ofthewidespreadandcommonspecies, and represents
indicator values were created by mathematically integrating data
from the original Ellenbergvalues and11compatibledata setsfor
other Europeanregions.Inaddition,weestimated indicator values
Alternative approaches to calculating Ellenberg-type indica-
tor value s from vegetatio n plots were pr oposed by ter Br aak and
values f rom vegetation p lots. ter Br aak and Grem men (1987) a lso
tion or cor rection. O ur experience f rom a previous s tudy (Chytr ý
etal.,2018) showsthatthe calculationofindicatorvalues for new
species from communitymeans can be negativelyaffected bythe
numberofspeciesinindividualplots.Forexample,only 477outof
TAB LE 1 RegionaldatasetsofEllenberg-typeindicatorvaluesusedasapotentialsourcefortheEuropeandataset
Data set Source Light Temperature Moisture Reaction Nutrients Salinity
Germany Ellenberg and
2478 2191 2407 3778 2315 2495
Austria Karrer(1992)1006 724 938 1198 855 1000
CantabrianRange Jiménez-Alfaroetal.(2021)NA NA NA NA NA
CzechRepublic Chy trýetal.(2018)2191 2194 2194 2192 2192 2194
European mires Hájeketal.(2020) 1479
France Julve(2015)3815 376 3 3750 3758 376 4 3792
GreatBritain Hilletal.(2000)1684 1684 1684 1684 1684
Greece(SouthAegean) Böhlingetal.(2002)NA NA 1831 NA NA 1922
Hungary Borhidi(1995)2028 2028 2028 2026 2028 2028
Italy GuarinoandLaRosa(2019)5136 4985 5092 4869 5049 5121
Saline habitats Dítěetal.(2023) 335
Switzerland/Alps Landoltetal.(2010)NC 4380 NC NC NC NC
Ukraine Didukh(2011)2877 NA 2895 NC NC NC
FINAL 8168 74 0 0 8030 7282 7193 7507
countsofspeciesoraggregates(afternomenclaturestandardization)withindicatorvalues.‘NA’(notaccepted),theindicatorvalueexist sandthe
Journal of Vegetation Science
TICHÝ et al.
usedforthisstudyoccurin morethan1%ofplots.Therearemany
vegetation plots in which these widespread species are the only spe-
thisconcerns10.4%ofall plots. As a result, somespecialized spe-
cies with missing indicator values may receive inappropriate values
similarly distributed species for calculating new indicator values
age all species in plots but assigns missing indicator values based on
averaging the valuesfora limitednumberofspecies withthemost
dicator valuesonly for speciesthat frequentlyco-occurwithother
more reliable.
Ellenberg(1974)an dotherauthorsdefinedindicatorva lu esonor-
Ellenbergetal. (2001)argued thatatleast part of theirscaleshave
equidistantsegmentation oftheintervalscale,whichallowsforcal-
thatco mmu nit ymeansca lculatedfr omindicatorva lue sb estestima te
environmental conditions when each indicator value is the centroid
ofthesymmetric(normallydistributed)speciesresponsecur vetothe
given envir onmental vari able. Other aut hors (Pignatt i et al., 2001;
data sets, Ellenberg indicator values can be evaluated with para-
metric tests because theytendtobenormallydistributed.Because
FIGURE 1 CorrelationmatrixofEllenberg-typeindicatorvaluesforEurope.Histogramsshowtherelativefrequencyofspeciesfora
blackdotrepresentsonespecies).***,p< 0.001;**,p< 0.01;*,p< 0.05.
Journal of Vegetation Science TICHÝ et al.
many recent studies have also estimated environmental conditions
usingcommunitymeans(e.g.Ahletal.,2021;Baumannet al., 2021;
ofpublishedin di catorv aluestobeinter valscales .D if ferencesamong
published sources were smoothed by calculating means with decimal
precision.The new datasetofindicatorvaluesretainstherange of
theoriginalEllenbergsc alesofnine,10or12degrees,soitiscompat-
Asourindicator-valuedatasetispreparedforbroad-sc aleanal-
yses, ituses a relativelycoarsetaxonomic resolution at thelevel
entsubspeciesofthe samespeciesordifferentnarrowly-defined
species within an aggregate may differ substantially in their
ecological requirements for some environmental variables (e.g.
ciesoccurring in theEVAvegetationplotshadan indicator value
lownumberofsuch species was thatonly a halfof the datasets
compatible with the Ellenberg scales (Hill et al., 2000; Böhling
The origi nal Ellenberg v alues had bee n estimated pr imarily by
expert knowledge. Cornwell and Grubb (2003) demonstrated that
FIGURE 2 CorrelationmatrixofthecommunitymeansofEllenberg-typeindicatorvaluesforEuropecalculatedforEVAvegetationplots.
pairwisecomparisonbetweentwocorrespondingindicators(eachblackdotrepresentsonevegetationplot).***,p< 0.001;**,p< 0.01;*,
p< 0.05.
Journal of Vegetation Science
TICHÝ et al.
Ellenbergspeciesvaluesfordifferentenvironmental conditionsare
often not independent. Forexample,they founda significant rank
correlation for the relationship between nutrients and moisture
(rs =0.3 62,p =0.001), whi chi salsofou ndi nou rha rmonizedd ataset
(Figure 1).Similartrendsoftherelationshipbetweenenvironmental
factor s can be seen i n Figure 2, where we compa red unweighted
communit y means calcu lated for vegetati on plots of the E VA da-
tabase.Thereason for thesignificanceofmost partial correlations
forcommunitymeans, inwhich theindicationof ecologicalfactors
Independent verificationof the validityofour dataset of indi-
cator values in relation to measured local environmental variables is
environmental conditions at the European scale at the sites where
the vegetation was sampled. The only exception is temperature,
which has both local and macroscale components considered in the
indicatorvalues. Therefore,the community-mean indicator values
canbecompare dwithinterpolate ddatafromtemperaturemeasure-
ments at cl imate stations . Such data repre sent macroclimate , but
Ellenberg(1974)alsoderivedtemp eratu rein dicatorvalu esfromspe-
cies’occurrenceinaltitudinalbeltsinGermanyandthe Alps.There
was a strong relationship between mean summer temperatures
tabase.However, we did not account for differences in local con-
ditions,such as slope, aspect, andshadingfrom trees, shrubs, and
using species co-occurrences showed negligible differences in R2
values(AppendixS3), as alsoshown inEwald (2003).Specieswith
were mainly rare species.
The 12 regional data sets of species indicator values in-
tegrated into our unified data set cover most of Central and
Western Europe. However, their reliability decreases with dis-
tance from their area of origin (Herzberger & Karrer, 1992;
Englisch & K arrer, 2001; Coudu n & Gégout, 2005; Godefro id &
Dana,2007), assome speciesmaychangetheirrealizednicheor
be represented bygenotypes adapted to different fundamental
niches(ecotypic adaptation; Hájkováet al.,2008). For example,
the nichewidth ofsome European species increasesnorthward,
making Ellenberg indicator values less applicable in Northern
species s hift and nar row their niche to wards the edge s of their
distributionrange (Papugaetal.,2018)relativetotheircentre of
comparisonsofregionaldata sets,which showedthe largestde-
viations fromtheoriginalEllenbergvalues fordatasets fromre-
gionsthataregeographicallyandclimatically farthestawayfrom
et al., 2021) and the South Aegean region of Greece (Böhling
FIGURE 3 RecommendedareaforapplicationoftheharmonizedEuropeandatasetofEllenberg-typeindicatorvalues.Europeisdivided
Journal of Vegetation Science TICHÝ et al.
apart of theirfundamental niche,resulting in thenarrowing of
usedthese datasetsfrom distantareas.Asa result,weconsider
thenewdatasetofindicatorvaluestobemainlyrepre sentativeof
CentralandWesternEurope, Italy and adjacent areas(Figure 3).
For the Ibe rian Peninsula , Greece, Turkey and othe r areas, new
systemsofecologicalindic atorvaluesneedtobedevelopedbased
Although the primary motivation for our work was to create
adata set of Ellenberg-type indicator values that can be used for
broad-scale international studies of macroecological patterns of
indicator values f rom a nearby region, o ur harmonized European
regional systems of indicator values provide more accurate esti-
For examp le, species that b ehave as generali sts on the Europ ean
scale and thus were not assigned an indicator value in the European
data set may have narrower niches and be good indicators in partic-
ularregions.Therefore,itis reasonable to continuetouseregional
systems of indicator values for local studiesinregionswhere such
systems exist. Nevertheless,iflocalstudiesfrom differentregions
use the Euro pean system of i ndicator valu es, their res ults can be
directly compared.
Axmanová standardized the nomenclature andprepared the data;
propose d analyses and p erformed all c alculations; L ubomír Tichý,
MilanChytrýandIrena Axmanová wrote the text;GabrieleMidolo
helped visualize the data presented in theappendices; all authors
commented on the manuscript.
WethankCajo terBraak for helpful comments on themanuscript,
Jan Divíšek for the first version ofthe climate data usedfor test-
program and with the funding organizations Technology Agency
of the Czech R epublic (SS700100 02), the Swiss Natio nal Science
Foundation SNF (project: FeedBaCks, 193907), and the German
Research Foundation (DFG BR 1698/21–1, DFG HI 1538/16–1).
Eduardo Fernández-Pascual was supportedbythe Jardín Botánico
Atlántico (SV-20-GIJON-JBA), Andraž Čarni, Urban Šilc and Filip
Küzmič were funded by Slovenian Research Agency (ARRS P1–
The vegetation-plot data used in this study are stored in the
Europea n Vegetation Archi ve database (E VA;http://eurov
eva-database) under project number 142, product (a). Tables of
originalindicatorvaluesforeach regionandharmonizedindicator
(, where future updates
binesEllenberg-type indicatorvaluesdeveloped herewithdistur-
banceindicatorvalues for European plants developedbyMidolo
Ellenberg-typeindicatorvaluesinaformatfortheJUICE program
(Tichý 2002) are available aty/juice/
Lubomír Tichý
Irena Axmanová
Jürgen Dengler
Riccardo Guarino
Florian Jansen
Gabriele Midolo
Michael P. Nobis
Koenraad VanMeerbeek https://orcid.
Svetlana Aćić
Fabio Attorre
Erwin Bergmeier
Idoia Biurrun
Gianmaria Bonari
Helge Bruelheide
Juan Antonio Campos
Andraž Čarni
Alessandro Chiarucci
Mirjana Ćuk
Renata Ćušterevska
Yakiv Didukh
Daniel Dí
Zuzana Dítě
Tetiana Dziuba
Giuliano Fanelli
Eduardo Fernández- Pascual https://orcid.
Emmanuel Garbolino
Rosario G. Gavilán
Jean- Claude Gégout
Behlül Güler
Journal of Vegetation Science
TICHÝ et al.
Michal Hájek
Stephan M. Hennekens
Ute Jandt
Anni Jašková
Borja Jiménez- Alfaro
Stephan Kambach
Dirk Nikolaus Karger
Gerhard Karrer
Ali Kavgacı
Ilona Knollová
Anna Kuzemko
Filip Küzmič
Flavia Landucci
Attila Lengyel
Jonathan Lenoir
Corrado Marcenò
Jesper Erenskjold Moeslund https://orcid.
Pavel Novák
Aaron Pérez- Haase
Tomáš Peterka
Remigiusz Pielech
Valerijus Rašomavičius
Solvita Rūsiņa
Arne Saatkamp
Urban Šilc
Željko Škvorc
Jean- Paul Theurillat
Thomas Wohlgemuth
Milan Chytrý
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Appendix S1.Percentages of indicator values in regional datasets
selectedasapotentialsource foraharmonizedEuropeandata set
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How to cite this article:Tichý,L.,A xmanová,I.,Dengler,J.,
Journal of Vegetation Science,34,e13168.Availablefrom:
... However, the more than 30 national and regional EIV systems lack consistency in scaling and coding of the ecological indicators, as well as in plant nomenclature, impeding analyses at the continental scale. These issues have partly been solved by the recently published pan-European EIV systems (Hájek et al. 2020;Midolo et al. 2023;Tichý et al. 2023) but their coverage of indicators and taxa, respectively, is far from complete. Thus, there is still an urgent need for an integrated and comprehensive EIV system for Europe. ...
... This system was then used for further comparisons, namely with the T values of the 23 source EIV systems that contained T. Moreover, we also compared EIVE 1.0 with the European T values recently proposed by Tichý et al. (2023). For all comparisons, Pearson correlations were calculated for the subset of species co-occurring in EIVE and the respective EIV system, and the most highly correlated bioclimatic variable was determined for both EIVE and the EIV system. ...
... Only in two cases did the original EIV-T values show higher correlations with other bioclimatic variables: bio5, i.e. "mean daily maximum air temperature of the warmest month" for "Austria_Pannonian" and bio8, i.e. "mean daily mean air temperatures of the wettest quarter" in the case of "Greece". Correlations of EIVE-T were in general higher than those of both the EIV-T values of the original EIV systems and the European Ellenberg-type indicator values (Tichý et al. 2023) (Table 3). The distribution of interregional niche width metrics (position range and position standard deviation) was very skewed, with many 0 values, whereas the distribution of intraregional metrics (average amplitude) showed multimodality. ...
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Aims: To develop a consistent ecological indicator value system for Europe for five of the main plant niche dimensions: soil moisture (M), soil nitrogen (N), soil reaction (R), light (L) and temperature (T). Study area: Europe (and closely adjacent regions). Methods: We identified 31 indicator value systems for vascular plants in Europe that contained assessments on at least one of the five aforementioned niche dimensions. We rescaled the indicator values of each dimension to a continuous scale, in which 0 represents the minimum and 10 the maximum value present in Europe. Taxon names were harmonised to the Euro+Med Plantbase. For each of the five dimensions, we calculated European values for niche position and niche width by combining the values from the individual EIV systems. Using T values as an example, we externally validated our European indicator values against the median of bioclimatic conditions for global occurrence data of the taxa. Results: In total, we derived European indicator values of niche position and niche width for 14,835 taxa (14,714 for M, 13,748 for N, 14,254 for R, 14,054 for L, 14,496 for T). Relating the obtained values for temperature niche position to the bioclimatic data of species yielded a higher correlation than any of the original EIV systems (r = 0.859). The database: The newly developed Ecological Indicator Values for Europe (EIVE) 1.0, together with all source systems, is available in a flexible, harmonised open access database. Conclusions: EIVE is the most comprehensive ecological indicator value system for European vascular plants to date. The uniform interval scales for niche position and niche width provide new possibilities for ecological and macroecological analyses of vegetation patterns. The developed workflow and documentation will facilitate the future release of updated and expanded versions of EIVE, which may for example include the addition of further taxonomic groups, additional niche dimensions, external validation or regionalisation.
Technical Report
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It took seven years and hundreds of hours of work by an international team of 34 authors to develop and publish the most comprehensive system of ecological indicator values (EIVs) of vascular plants in Europe to date. EIVE 1.0 is now available as an open access database ( and described in the accompanying paper (Dengler et al. 2023). EIVE 1.0 provides the five most-used ecological indicators, M – moisture, N – nitrogen, R – reaction, L – light and T – temperature, for a total of 14,835 vascular plant taxa in Europe, or between 13,748 and 14,714 for the individual indicators. For each of these taxa, EIVE contains three values: the EIVE niche position indicator, the EIVE niche width indicator and the number of regional EIV systems on which the assessment was based. Both niche position and niche width are given on a continuous scale from 0 to 10, not as categorical ordinal values as in the source systems.
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We present the first standardized list of the vascular flora of the Cantabrian Mountains, a transitional zone between the Eurosiberian and Mediterranean biogeographic regions in northwestern Spain. The study area comprises 15000 km2 divided in UTM grid cells of 10 km x 10 km, for which we revised occurrence data reported in the Spanish Plant Information System (Anthos) and the online database of Iberian and Macaronesian Vegetation (SIVIM). We used a semi-automatic procedure to standardize taxonomic concepts into a single list of names, which was further updated by expert-based revision with the support of national and regional literature. In the current version, the checklist of the Cantabrian Mountains contains 2338 native species and subspecies, from which 56 are endemic to the study area. The nomenclature of the checklist follows Euro+Med in 97% of taxa, including annotations when other criteria has been used and for taxa with uncertain status. We also provide a list of 492 non-native taxa that were erroneously reported in the study area, a list of local apomictic taxa, a phylogenetic tree linked to The Plant List, a standardized calculation of Ellenberg Ecological Indicator Values for 80% of the flora, and information about life forms, IUCN threat categories and legal protection status. Our review demonstrates how the Cantabrian mountains represent a key floristic region in southern Europe and a relevant phytogeographical hub in south-western Europe. The checklist and all related information are freely accessible in a digital repository for further uses in basic and applied research.
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Question Malus sylvestris is considered an endangered tree species in Central Europe. Hybridization with Malus domestica poses a serious threat to the genetic integrity of the wild species. Here we investigate whether M. sylvestris and the hybrid M. domestica × sylvestris occur in the same habitat or have different ecological niches and whether M. sylvestris is threatened by displacement by the hybrid. Location Northern Bavaria Methods Taxon delimitation using six genetic microsatellite markers and 613 Germany-wide references of M. sylvestris and 75 cultivars. To determine differences in the ecological niches between M. sylvestris and hybrids, light availability for the trees was estimated via gap fractions in hemispherical photographs. Soil particle size fractions and pH values were determined for every horizon. Vegetation relevé data was collected, and mean Ellenberg indicator values were calculated. For habitat differences, means in combination with frequency patterns of the parameters were compared, logistic models and Detrended correspondence analysis (DCA) of community data were calculated. Results Genetic markers identified 22 M. sylvestris and 11 hybrids, meaning that in the study area the wild taxon is much more frequent than the hybrid. Ecological site differences between the M. sylvestris and its hybrid with M. domestica were best explained by light availability, pH and mean Ellenberg moisture value. In contrast to the ecological demand of the hybrid, Malus sylvestris tolerated wet soil and flooding and even somewhat shadier conditions in the later successional stages. DCA revealed that differences in the composition of plant communities of the taxa were primarily driven by soil moisture. Conclusions Our data suggested different ecological niches, which are appropriate to reduce the risk of replacement of M. sylvestris by the hybrid M. domestica x sylvestris. Hence, these findings provide important implications for a more targeted planning of in-situ-conservation strategies of M. sylvestris genomes with low levels of admixture and help to protect plant communities suitable for the threatened wild apple.
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Questions How have species composition and diversity in the herb and bryophyte layer of spruce forest communities developed on unlimed plots and on plots with varying liming intensity under the conditions of decreasing sulphur (S) deposition and unabated high nitrogen (N) deposition? Location Spruce stands in the Erzgebirge, East Germany Methods We repeated herb and bryophyte layer surveys on 36 quasi-permanent plots after approx. 23 years (1989/1991 vs. 2012/2014). Over this period, the study area experienced a sharp decline in S deposition and repeated liming of the spruce stands. Our plots represent a gradient of liming from zero to four applications. We analyse the change in species composition and species diversity depending on the liming intensity using Ellenberg indicator values (EIV), correspondence analysis, indicator species analysis and diversity parameters (alpha diversity, Lennon similarity). Results On the unlimed plots, the species composition of the herb layer remained unchanged over the study period, despite the coverage of Calamagrostis villosa and Deschampsia flexuosa and the total coverage of the herb layer, which decreased. In contrast, the bryophyte layer showed a significant increase in coverage and species richness with the decrease in S deposition. Liming led to a sharp increase in species richness in the herb layer, due to the appearance of disturbance indicators. The mean EIV-R and EIV-N also significantly increased. All observed effects increased with increasing liming intensity. We also noticed an increase in species richness over time in the bryophyte layer on the limed plots. However, this was less pronounced than on the unlimed plots, and decreased with increasing liming intensity. This was because the appearance of disturbance indicators or of typical species for the Calamagrostio villosae-Piceetum was partially compensated for by the disappearance of acid indicators as a result of liming. The species turnover was thus much higher in the bryophyte layer than in the herb layer. Conclusions In contrast to the herb layer, the bryophyte layer proves to be a sensitive indicator of changes in air pollution. The effects of greatly reduced S deposition on the bryophyte layer outweigh those of continuous N deposition in the study area. Liming acts as a disturbance in both the herb layer and the bryophyte layer. This influence increases with increasing liming intensity. Our results do not support a general application of liming in large, contiguous forest areas. Rather, this measure should be applied in a differentiated manner.
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We introduce and provide a new data-set of species-specific ecological indicator values (alternative ‘Ellenberg values’ for climatic variables, light, moisture, soil reaction/pH, salinity, nitrogen and phosphorus plus response to management and disturbance), physiological and reproductive traits (including symbionts, phenology, pollinators, nectar production, seed properties), indices of relevance for conservation (including introduction history and biodiversity relevance), and presence in 38 main vegetation types for all vascular plant species of Sweden based on a broad survey of published and unpublished data. The potential uses and limitations of the values, traits and indices are briefly outlined and discussed.
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The distribution of large ungulates in space is in large extent driven by the availability of forage, which in temperate forests depends on light availability, and associated plant diversity and cover. We hypothesized that the increased number of GPS fixes of European bison (Bison bonasus L.) in usually avoided spruce forests was an effect of higher plant species richness and cover of the forest floor, which developed owing to increased light availability enhanced by spruce mortality. We carried out 80 forest floor plant surveys combined with tree measurement on plots chosen according to the number of GPS locations of GPS-collared European bison. The mean plant species richness per plot was higher on intensively visited plots (IV) than rarely visited (RV) plots (30 ± 5.75 (SD) versus. 26 ± 6.19 (SD)). The frequency of 34 plant species was higher on IV plots, and they were mainly herbaceous species (32 species), while a significant part of 13 species with higher frequency on RV plots was woody plants (5 species). The species richness of forbs was higher on IV plots, while other functional groups of plants did not differ. Tree stem density on the IV plots was lower than on the RV plots (17.94 ± 6.73 (SD) versus 22.9 ± 7.67 (SD)), and the mean value of Ellenberg's ecological indicator for light availability for all forest floor plant species was higher on IV plots. European bison visiting mature spruce forests was driven by higher forest floor plant cover and species richness, and high share and species richness of forbs. The two latter features may be translated into higher quality and diversity of forage. In spite of morphological characteristics suggesting that European bison is a species of mixed (mosaic) habitats, it seems to be well adapted to thrive in diverse forests.
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Aim The EUNIS Habitat Classification is a widely used reference framework for European habitat types (habitats), but it lacks formal definitions of individual habitats that would enable their unequivocal identification. Our goal was to develop a tool for assigning vegetation‐plot records to the habitats of the EUNIS system, use it to classify a European vegetation‐plot database, and compile statistically‐derived characteristic species combinations and distribution maps for these habitats. Location Europe. Methods We developed the classification expert system EUNIS‐ESy, which contains definitions of individual EUNIS habitats based on their species composition and geographic location. Each habitat was formally defined as a formula in a computer language combining algebraic and set‐theoretic concepts with formal logical operators. We applied this expert system to classify 1,261,373 vegetation plots from the European Vegetation Archive (EVA) and other databases. Then we determined diagnostic, constant and dominant species for each habitat by calculating species‐to‐habitat fidelity and constancy (occurrence frequency) in the classified dataset. Finally, we mapped the plot locations for each habitat. Results Formal definitions were developed for 199 habitats at Level 3 of the EUNIS hierarchy, including 25 coastal, 18 wetland, 55 grassland, 43 shrubland, 46 forest and 12 man‐made habitats. The expert system classified 1,125,121 vegetation plots to these habitat groups and 73,188 to other habitats, while 63,064 plots remained unclassified or were classified to more than one habitat. Data on each habitat were summarized in factsheets containing habitat description, distribution map, corresponding syntaxa and characteristic species combination. Conclusions EUNIS habitats were characterized for the first time in terms of their species composition and distribution, based on a classification of a European database of vegetation plots using the newly developed electronic expert system EUNIS‐ESy. The data provided and the expert system have considerable potential for future use in European nature conservation planning, monitoring and assessment.
Motivation: Indicator values are numerical values used to characterize the ecological niches of species and to estimate their occurrence along gradients. Indicator values on climatic and edaphic niches of plant species have received considerable attention in ecological research, whereas data on the optimal positioning of species along disturbance gradients are less developed. Here, we present a new data set of disturbance indicator values identifying optima along gradients of natural and anthropogenic disturbance for 6382 vascular plant species based on the analysis of 736,366 European vegetation plots and using expert-based characterization of disturbance regimes in 236 habitat types. The indicator values presented here are crucial for integrating disturbance niche optima into large-scale vegetation analyses and macroecological studies. Main types of variables contained: We set up five main continuous indicator values for European vascular plants: disturbance severity, disturbance frequency, mowing frequency, grazing pressure and soil disturbance. The first two indicators are provided separately for the whole community and for the herb layer. We calculated the values as the average of expert-based estimates of disturbance values in all habitat types where a species occurs, weighted by the number of plots in which the species occurs within a given habitat type. Spatial location and grain: Europe. Vegetation plots ranging in size from 1 to 1000 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.
Ellenberg indicator values for plant species are widely used metrics in ecology, providing a proxy measure of environmental conditions, without direct measurements. They integrate environmental conditions over time since species will only persist where conditions are favourable. Ellenberg moisture (F) values summarise the hydrological environment experienced by plants. However, the relationship between indicator values and hydrological metrics appears to be influenced by a range of other abiotic and biotic factors, limiting our ability to fully interpret Ellenberg F. Focussing on Ellenberg F, we evaluated how the unweighted mean plant community F value to hydrology, specifically water table depth, is influenced by other environmental factors, ground cover type and alpha diversity in UK seasonal coastal wetlands (dune slacks). As expected, water table depth had the strongest influence on unweighted mean Ellenberg F. We show that unweighted mean Ellenberg F was more sensitive to changes in water table levels for plant communities that were more nutrient limited, when the organic matter layer was thicker and there was less bare ground cover. Unweighted mean Ellenberg F was consistently lower for a given water table depth, when there was lower atmospheric nitrogen deposition, lower loss of ignition (a measure of organic matter content) and more diverse plant communities. These findings help us to better interpret what Ellenberg F indicator values tell us about hydrological conditions, by understanding the factors which alter that relationship.
Bioindication systems based on the occurrence of plant species are widely used in vegetation science, palaeoecology, community ecology, geographical modelling and global change biology. Although the existing systems are mostly regional, the development of large-scale vegetation databases calls for the establishment of a pan-European indication system. Here we present the first step towards this goal, by assessing indicator values for soil moisture and water table depth in European mires and associated grasslands. For each vascular plant and bryophyte species occurring in 24,091 vegetation-plot records of European mires, we developed an updated system of Ellenberg-like Ecological Indicator Values (EIVs) at the scale of 1–12 (species optimum) including ecological valences – minimum value (drought intolerance), maximum value (flooding tolerance) and range of values (tolerance), using both statistical and expert-based approaches for species co-occurrence data. Spearman correlation coefficients, Akaike Information Criterion (AIC) and Akaike weights were used to assess the performance of the updated EIVs against measured soil moisture and water table in two Central-European datasets, and against the proportion of aquatic mollusc species in a European-scale dataset. The updated EIVs performed well in all cases, having stronger correlations with directly measured water table and the combined proportion of aquatic and wetland molluscs than the original EIVs. The original EIVs, however, performed better with soil moisture. In the Central-European datasets, the use of minimum values (drought intolerances) of the updated EIVs weighted by species covers and/or tolerances led to the highest correlations with water table in all cases. This improvement of th