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Questions: Which environmental factors influence fine-grain beta diversity of vegetation and do they vary among taxonomic groups? Location: Palaearctic biogeographic realm. Methods: We extracted 4,654 nested-plot series with at least four different grain sizes between 0.0001 m² and 1,024 m² from the GrassPlot database, covering a wide range of different grassland and other open habitat types. We derived extensive environmental and structural information for these series. For each series and four taxonomic groups (vascular plants, bryophytes, lichens, all), we calculated the slope parameter (z-value) of the power-law species–area relationship (SAR), as a beta diversity measure. We tested whether z-values differed among taxonomic groups and with respect to biogeographic gradients (latitude, elevation, macroclimate), ecological (site) characteristics (several stress-productivity, disturbance and heterogeneity measures, including land use) and alpha diversity (c-value of the power-law SAR). Results: Mean z-values were highest for lichens, intermediate for vascular plants and lowest for bryophytes. Bivariate regressions of z-values against environmental variables had rather low predictive power (mean R² = 0.07 for vascular plants, less for other taxa). For vascular plants, the strongest predictors of z-values were herb layer cover (negative), elevation (positive), rock and stone cover (positive) and the c-value (u-shaped). All tested metrics related to land use (fertilisation, livestock grazing, mowing, burning, decrease in naturalness) led to a decrease in z-values. Other predictors had little or no impact on z-values. The patterns for bryophytes, lichens and all taxa combined were similar but weaker than those for vascular plants. Main conclusions: We conclude that productivity has negative and heterogeneity positive effects on z-values, while the effect of disturbance varies depending on type and intensity. These patterns and the differences among taxonomic groups can be explained via the effects of these drivers on the mean occupancy of species, which is mathematically linked to beta diversity.
J Veg Sci. 2021;32:e13045.    
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Journal of Vegetation Science
  Revised:1M ay2021 
DOI: 10.1111/jvs.130 45
Fine- grain beta diversity of Palaearctic grassland vegetation
Iwona Dembicz1,2,3 | Jürgen Dengler2,3,4 | Manuel J. Steinbauer5|
Thomas J. Matthews6,7 | Sándor Bartha8,9 | Sabina Burrascano10 |
Alessandro Chiarucci11 | Goffredo Filibeck12 | François Gillet13 |
Monika Janišová14 | Salza Palpurina15,16 | David Storch17,1 8 | Werner Ulrich19 |
SvetlanaAćić20 | Steffen Boch21 | Juan Antonio Campos22 |
Laura Cancellieri23 | Marta Carboni24 | Giampiero Ciaschetti25 |
Timo Conradi3| Pieter De Frenne26 | Jiri Dolezal27 | Christian Dolnik28|
Franz Essl29 | Edy Fantinato30 | Itziar García- Mijangos22 |
Gian Pietro Giusso del Galdo31 | John- Arvid Grytnes32 | Riccardo Guarino33 |
Behlül Güler34 | Jutta Kapfer35 | Ewelina Klichowska36|ŁukaszKozub1|
Anna Kuzemko37 | Swantje Löbel38| Michael Manthey39 |
Corrado Marcenò15,22 | Anne Mimet40,2 | Alireza Naqinezhad41 |
Jalil Noroozi42 | Arkadiusz Nowak43,44 | Harald Pauli45,46 | Robert K. Peet47 |
Vincent Pellissier40 | Remigiusz Pielech48,49 | Massimo Terzi50 |
EminUğurlu51 | Orsolya Valkó52 | Iuliia Vasheniak53 | Kiril Vassilev16 |
Denys Vynokurov37 | Hannah J. White54 | Wolfgang Willner42 |
Manuela Winkler45,46 | Sebastian Wolfrum55,56 | Jinghui Zhang57, 3 |
Idoia Biurrun22
1Department of Ecology and Environmental Conservation, Institute of Environmental Biology, Faculty of Biology, University of Warsaw, Warsaw, Poland
2VegetationEcologyGroup,InstituteofNaturalResourceSciences(IUNR),ZurichUniversit yofAppliedSciences(ZHAW),Wädenswil,Switzerland
3Plant Ecology, Bayreuth Center of Ecology and Environmental Research (BayCEER), University of Bayreuth, Bayreuth, Germany
5Sport Ecology, Department of Sport Science & Bayreuth Center of Ecology and Environmental Research (BayCEER), University of Bayreuth, Bayreuth,
7GEES (School of Geography, Earth and Environmental Sciences) and Birmingham Institute of Forest Research, University of Birmingham, Birmingham, UK
11BIOMELab,DepartmentofBiological,GeologicalandEnvironmentalSciences(BiGeA),AlmaMaterStudiorum–Universit yofBologna,Bologna,Italy
provided the original work is properly cited.
©2021TheAuthors.Journa l of Vegetation SciencepublishedbyJohnWiley&SonsLtdonbehalfofInternationalAssociationforVegetationScience
Thisar ticleisapartoft heSpecialFeat ureMacroecol ogyofvegetation,edi tedbyMeelisPär tel,Fr ancescoMariaS abatini,NaiaM oruet a-Holme,H olgerKreftan dJürgenDengler.
2 of 15 
Journal of Vegetation Science DEMBIC Z Et al.
14InstituteofBotany,PlantScienceandBiodiversit yCenter,SlovakAcademyofSciences,BanskáBystrica,Slovakia
17CenterforTheoreticalStudy,CharlesUniversit y,Praha1,CzechRepublic
18Depar tmentofEcology,FacultyofScience,CharlesUniversity,Praha2,CzechRepublic
19Depar tment of Ecology and Biogeography, Faculty of Biology and Environmental Protection, Nicolaus Copernicus University, Torun, Poland
20Depar tmentofAgrobotany,FacultyofAgriculture,UniversityofBelgrade,Belgrade-Zemun,Serbia
21Biodiversity&Conser vationBiology,WSLSwissFederalResearchInstitute,Birmensdorf,Switzerland
22PlantBiologyandEcology,UniversityoftheBasqueCountr yUPV/EHU,Bilbao,Spain
23Depar tmentofAgriculturalandForestr ySciences(DAFNE),UniversityofTuscia,Viterbo,It aly
24Department of Science, University of Roma TRE, Rome, Italy
28Ecology Centre Kiel, Kiel University, Kiel, Germany
30Department of Environmental Sciences, Informatics and Statistics, Ca' Foscari University of Venice, Venice, Italy
31Department of Biological, Geological and Environmental Sciences, University of Catania, Catania, Italy
32Department of Biological Sciences, University of Bergen, Bergen, Norway
33Dipartimento di Science, Università degli studi di Palermo, Palermo, Italy
36InstituteofBotany,FacultyofBiology,JagiellonianUniversit y,Kraków,Poland
37Depar tmentofGeobotanyandEcology,M.G.KholodnyInstituteofBotany,NationalAcademyofSciencesofUkraine,Kyiv,Ukraine
41Depar tmentofPlantBiology,FacultyofBasicSciences,UniversityofMazandaran,Babolsar,Iran
42Depar tmentofBotanyandBiodiversityResearch,UniversityofVienna,Vienna,Austria
44Institute of Biology, University of Opole, Opole, Poland
45Depar tmentofIntegrativeBiologyandBiodiversit yResearch,GLORIACo-ordination,UniversityofNaturalResourcesandLifeSciencesVienna(BOKU),
47Depar tmentofBiology,UniversityofNorthC arolina,ChapelHill,NorthCarolina,USA
50Institute of Biosciences and Bioresources (IBBR), Italian National Council of Research (CNR), Bari, Italy
51Forest Engineering, Faculty of Forestry, Bursa Technical University, Yildirim, Turkey
52CentreforEcologicalResearch,InstituteofEcologyandBotany,MTA-ÖKLendületSeedEcologyResearchGroup,Vácrátót ,Hungary
53Faculty of Chemistry, Biology and Biotechnologies, Vasyl' Stus Donetsk National University, Vinnytsia, Ukraine
54School of Biology and Environmental Science, Earth Institute, University College Dublin, Dublin 4, Ireland
Institute of Natural Resource Sciences
Questions: Whichenvironmental factorsinfluence fine-grain betadiversity of veg-
etation and do they vary among taxonomic groups?
Location: Palaearctic biogeographic realm.
Methods: Weextracted 4,654 nested-plot serieswith at least four different grain
 3 of 15
Journal of Vegetation Science
One of the central aims of ecology and evolutionary biology is to
understand the drivers of biological diversity at different spatial and
temporalscales(Allanet al., 2011;Isbell et al.,2011).Acrucialdi-
mension of biological diversity is β-diversity,thevariabilityinspecies
compositionbetweenlocalcommunities(Andersonetal., 2011).At
large spat ial grain sizes (≥ 100 km ²) and along lat itudinal and e le-
vation gradients, important drivers of β-diversityare macroclimate
et al., 2018). At medium (0.01 km² to <100 km²) and small spatial
grainsizes(<0.01kor1ha;grainsizeclassificationmodifiedf rom
Field et al., 2009), the drivers are much less understood, although
microclimateandsoilvariability areknowntoinfluencesmall-scale
communitycomposition(Opedalet al., 2015; Ulrichetal.,2017).A
port a more informed application of this biodiversity dimension in
vegetation ecology, conservation and management measures, and
allow more reliable inter- and extrapolations of species richness
to other fine grain sizes. Transferring results from coarse-grain
β-divers ity studies is n ot possible, as sever al studies have show n
strong changes in patterns and drivers of β-diversity across grain
cies richness with area are another major research focus of ecology
and biogeo graphy (Connor & Mc Coy,1979; Dra kare et al., 200 6;
Dengler, 20 09). SARs can be co nstructe d in various ways, a mong
them, wi th nested and no n-nested samp ling units (Den gler et al.,
2020a). There is growing evidence that among the numerous pro-
tion (S = c Az log S = log c + z log A; where S is species richness,
A is area, and c and z are fitted parameters) provides the best fit in
most cases(Connor&McCoy, 1979;Dengler,2009; Triantis etal.,
2012; Matt hews et al., 2016; Den gler et al., 2020 a). The param e-
tersofSARfunctions(andspecificallytheexponentz of the power
groups with different dispersal abilities (Patiño et al., 2014), assess-
ing the impact of anthropogenic disturbance on species assemblages
(Tittensor et al., 2007), and quantifying the expected species loss
Funding information
of Ecology and Environmental Research
(BayCEER) funded the initial GrassPlot
workshop during which the database was
established and the current paper was
suppor t from the Polish National Science
Centre (grant 2017/27/B/NZ8/00316). IB,
232/2016). SBa was suppor ted by the
G I N O P - 2 . 3 . 2 - 1 5 - 2 0 1 6 - 0 0 0 1 9  p r o j e c t . 
was supported by the Polish National
NZ8/03234) and by a Swiss Government
by the National Research Foundation
of Ukraine (project no. 2020.01/0140).
Co-ordinating Editor:HolgerKreft
range of different grassland and other open habitat types. We derived extensive
environmental and structural information for these series. For each series and four
taxonomic groups (vascular plants, bryophytes, lichens, all), we calculated the slope
parameter (z-value)ofthepowerlawspecies–arearelationship(SAR),asabetadiver-
sity measure. We tested whether z-valuesdifferedamongtaxonomicgroupsandwith
respect to biogeographic gradients (latitude, elevation, macroclimate), ecological (site)
characteristics (several stress– productivity, disturbance and heterogeneity measures,
including land use) and alpha diversity (c-valueofthepowerlawSAR).
Results: Mean z-values were highest for lichens, intermediate for vascular plant s
andlowestfor bryophytes.Bivariateregressions of z-values against environmental
variables had rather low predictive power (mean R² = 0.07 for vascular plants, less
for other taxa). For vascular plants, the strongest predictors of z-values wereherb
layer cover (negative), elevation (positive), rock and stone cover (positive) and the c-
mowing, burning, decrease in naturalness) led to a decrease in z-values.Otherpredic-
tors had little or no impact on z-values.Thepatternsforbryophytes,lichensandall
taxa combined were similar but weaker than those for vascular plants.
Conclusions: We conclude that productivity has negative and heterogeneity posi-
tive effects on z-values,while the effect of disturbance varies depending on type
and intensity. These patterns and the differences among taxonomic groups can be
explained via the effects of these drivers on the mean occupancy of species, which is
mathematically linked to beta diversity.
occupancy, Palaearctic grassland, productivity, scale dependence, species– area relationship
4 of 15 
Journal of Vegetation Science DEMBIC Z Et al.
While β-diversity and SARs are widely studied, there is little
awarenessthatthese twoconceptsarecloselyrelated. MacArthur
(1965) implicitly suggested that the slope parameter z of nested
measure of α-diversity,butthiswaslaterdismissedbyConnor and
McCoy (1979). Koleff et al. (2003) demonstrated mathematically
that the exponent z of the power function is indeed a direct mea-
sure of β-diversity.Similarly,Ricotta etal. (2002)proposedtheuse
of the slope parameter b1ofspeciesaccumulationcurves(SACs;for
differences fromSARs,see Dengler et al.,2020a) modelledwith a
logarithmic function (S = b0 + b1 log A) as a measure of multiplicative
and multiplicative β-diversity,butindicatedthattheyareonlyrarely
More recen tly,P olyakova et al. (2016; see al so Sreekar et al.,
function, the slope parameter z is calculated by:
where S2 and S1arethesp eciesrichn essvalu esofthegr ainsizesA2 and
A1, respectively, with A2 > A1. Therefore, if the sampling takes place in
nested plots, S2 can be interpreted as γ-diversityandS1 as (averaged)
Defining multiplicative β-diversityas
it follows that
plicative β-diversity,dividedbythelogarithmoftheratioofthecon-
sidered areas. The advantage of this approach is that the resulting
value allows direct comparison of β-diversityvaluesirrespective of
the relative increase in area between the α-andγ- l e v e l .
The slope zofnestedpowerfunctionSARswithinacontinuous
ferent, spatially separate unit) is also linked to the average sparsity
of species (Storch, 2016) in terms of the proportion of occupied sub-
plots: the sparser the species are on average in the sampling plots
Intuitively, if all species occur in each subplot of a larger plot, the
SAR slope approacheszero, while if all species exclusivelyoccupy
just one subplot, the slope approaches one. There is a mathematical
relationship between mean species’ occupancy and the SAR slope
quire complete information on all species occupancies within a given
plot (i.e. the total number of occupied subplots for each species),
whichisnotavailableinmostnested-plot data(usuallyonly a very
small subset of all potential subplots ofsmallergrainsize within a
larger plot is sampled, thus precluding a realistic estimate of occu-
pancy). Still, one can predict that any factor affecting mean species
occupan cy in a sampling d esign will also infl uence the SAR slo pe
(Šizling & Storc h, 2004). This f inding enables t he investigatio n of
the effects of taxonomic group and ecological factors on species
largely i diosyncrat ic and inconclu sive (Appen dix S1).Fo r instance,
the occupancy of grassland plant species, creating opportunities for
others (Loucougarayet al.,2004), thuspossibly increasing the SAR
slope. In contrast, other disturbances may selectively eliminate the
sparsest species, increasing overall mean species occupancy, and
thus decreasing theSARslope.Inthis context of multiple possible
shed light on the causal pathways through which individual environ-
Grasslands are inherently fine-grain communities with the
maximum compositional variability appearing at very fine scales,
usually b elow 1 m² (Bart ha et al., 200 4, 2011). The vegetatio n of
trasting ecological properties (vascular plants, bryophytes, lichens).
(e.g. from sea level to more than 5,000 m a.s.l., from very wet to
intensified; Dengler et al., 2020b). Since Palaearctic grasslands are
known toexhibitextremevariationinsmall-scale species richness,
from monospecific systems to the world records in vascular plant
species richnessbelow 100 m² (Wilsonetal.,2012; Dengler et al.,
2016a),weexpectthatfine-grain β-diversity values willalsocover
a broad range.
Here, we usethe extensiveGrassPlot database (Dengler etal.,
2018), which prov ides multi-scal e species richnes s data of grass-
grain β-diversity, but the oretical pred ictions for the r ole of other
environmental factors were unclear due to their possible contradic-
tory ef fects (se e Appendix S1). Thus , we addressed th e following
research questions:
1. How do z-values differ among three taxonomic groups (vascular
plants, bryophytes, and lichens)?
mult =
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Journal of Vegetation Science
2. Howdoz-valuesvaryinrelationtolarge-scalebiogeographic char-
acteristics, such as latitude, elevation and macroclimate?
3. Howare z-valuesrelatedtosmall-scaleecological characteristics,
related to stressproductivity, disturbance and heterogeneity?
4. Howarez-valuesrelatedtoα- diversity?
2.1 | Vegetation-plotdata
Weusedplotdatafromthe collaborativevegetation-plotdatabase
GrassPlot (Biurrun et al., 2019; Dengler et al., 2018; https://edgg.
org/databases/GrassPlot) registered as EU-00-003 in the Global
Index of Vegetation-Plot Databas es (GIVD; Dengler et al., 2011).
logical, environmental and structural information from grasslands
and other n on-forest veget ation type s (rocks and scre es, deser ts,
ruderal communities etc.) from the Palaearctic biogeographic realm.
sampling schemes (e.g. Dengler et al., 2016b) with areas from 0.0 001
serie s,consistingof16 4,578individualplots).Allserieshadinforma-
tion on vascular plants,890onterricolous(soil-dwelling)bryophytes,
894 on terricolous lichens, and 862 on all three taxonomic groups,
i.e. the total species richness of the vegetation (hereafter termed
complete vegetation). We refer to the four categories (complete veg-
etation, vascular plants, bryophytes, lichens) together as the four
taxonomic groups.
For those n ested-plot ser ies with more th an one plot for cer-
obtainedone single richness valueforeachgrain sizewithin each
nested-plot series a nd for each taxon omic group. The p lots were
distributed across 34 different countries from 28.5° N to 70.0° N
and 16.2° W to 161.8° E, and covered an elevation gradient from
0mto 4,387 m a.s.l. (Figure 1 and Appendix S2).They includeda
wide range of different vegetation types (natural grasslands, sec-
and ruderal communities andsemi-desert s); in fact, the selection
criteria of GrassPlot (Dengler et al., 2018) include 63% of all distin-
guished h abitat typ es in the Europea n part of the rea lm (Janssen
etal.,2016).FIGURE1 Densityandspatialdistributionofthe4,654
nested-plotseries in thePalaearctic biogeographicrealm contain-
ing information on vascular plant species that were analysed in this
study. The colour scale indicates the number of available series per
10,000-km²grid cell.The mapuses theLambert Azimuthal Equal-
2.2 | SARmodelling
We fitted a power function to each data set representing a taxo-
S-space”(S = c Az, where S is species richness, Aisareain m²,and
c and z are fitted parameters) and the “logarithmic S-space”(log10
S = log10 c + z log10 A). Both approaches are valid, have been widely
used in the literature, and have different strengths and limitations
(see Dengler, 2009; Dengler et al., 2020a). Due to the different
treatment of the error structure, the parameter estimates in the two
mathematical spaces usually deviate. Generally, fitting in S- s p a c e 
log S-spacegivesmoreweighttogoodfitatsmallergrainsizesand
typically reduces heteroscedasticity in the residuals.
To fit the power model in log S-space,weusedlinearregression
and the standard ‘lm’ function in R (R Core Team, 2018). The fit-
ting in S-spacefollowedtheapproachofDengleretal.(2020a; see
the ‘mle2’ function in the bbmle R package (Bolker & R Core Team,
2017). Starting parameter values were derived from fitting the linear
model in log S-space.Inasmallnumberofcaseswheretheresultant
ent starting parameter values to achieve convergence (see Dengler
et al., 2020a). To avoid problems with fitting in log S-space, we as-
signed small non-zero values to any subplot with observed values
of S = 0 (see Dengler et al., 2020a). For both the S-space andlog
FIGURE1 Densityandspatialdistributionofthe4,654nested-
plot series in the Palaearctic biogeographic realm containing
information on vascular plant species that were analysed in this
study. The colour scale indicates the number of available series per
6 of 15 
Journal of Vegetation Science DEMBIC Z Et al.
2.3 | Predictorvariables
In addition to the taxonomic group, we used a wide range of plot
characteristics available from GrassPlot and related to our re-
S3, for the number of plots used in each analysis see Appendix
S6). We grouped them into three categories: biogeographic char-
acteristics, ecological characteristics and α- diversity. The ecological
characteristics were further subdivided into those related to the
stress– productivity and disturbance axes (Grime, 1977; Huston,
2014) as well as to heterogeneity (Lundholm , 2009; Stein et al .,
2014), in order to conn ect with well-es tablished th eories of α-
weakly connected to the respective group or might contain ele-
ments of more than one group.
Asbiogeographic characteristics, we used two variables related to
major biogeographic theories (latitude and elevation) and four major
climatic variables (mean annual temperature, temperature seasonality,
mean annual precipitation, precipitation seasonality). While latitude
and most of the elevation data were provided by the original data
set collectors, missing elevation data and the other four variables
were derived from external sources using the plot coordinates (for
The stress– productivity variables refer to the stress– productivity
axis of Gri me (1977; product ivity in Huston , 2014): We used soil
pH and soil depth mean as soil-related stress measures,assuminga
U-shapedrelationshipofstress withsoilpH(nutrientuptakeislim-
pH;see Lambers etal.,2008)and a negative relationship withsoil
receive (anthropogenic) fertilization vs those that do not. Finally,
we used herb layer cover as a proxy of productivity. While at cover
values below 90% there should be a reasonably good correlation of
standing biomass with herb layer cover (Ónodi et al., 2017), we ac-
knowledge that for very high cover values the relationship likely will
disappear as the biomass then mainly is determined by vegetation
The disturbance variables refer to disturbance sensu Grime
cumulatedbio-and necromass.Therefore, litter cover was used
asanadverseproxy of disturbance(Appendix S1).Wealso con-
sider slope inclination as related to disturbance because erosion
increases with inclination. Furthermore, we extracted the follow-
ing measures of anthropogenic disturbance from GrassPlot: nat-
uralness (at two levels) and presence of the management types
livestock grazing, mowing and burning. Naturalness at coarse level
indicates whether grassland is natural or secondary, while natu-
ralness at fine level refers to the intensity of human impact on
vegetation within each of the two coarse categories (for details,
The heterogeneityv ariables are tho se that describe t he small-
scale variability of stress– productivity and/or disturbance, usually
determined withinthe largest orsecond-largestgrainplot of each
nested series: Soil depth CV indicates the variability of soil depth
within a plot; microtopography refers to deviations from a smooth
plane, which couldlead to small-scale differencesin soil moisture;
rock and stone cover is related to variation in soil depth, microclimate
and erosion; shrub layer cover is related mainly to variation in light
and moisture conditions.
As a measure of α- diversity, we used the c- value fromthe SAR
modelling (see above). The c- value is the predicted average species
2.4 | Analysesofthez- values
We tested how the modelled z-value s of the power funct ion de-
pended on our four groups of predictors: taxonomic group, bio-
geographic characteristics, ecological (site) characteristics and
α-diversity.Weexcluded nested-plot series with no reported spe-
cies for the investigated taxonomic group as well as the very few
nested-plotseries where the model fitting didnotconvergeorre-
sulted in theoretically impossible values of z > 1 (Williamson, 2003).
In consequence, for S- space we had esti mated z-values for 4 ,554
series for vascular plants, 716 for bryophytes, 400 for lichens and
862 for complete vegetation (numbers differ slightly for log S-space).
As only a small fractionof our data set contained all variables
of interest, we decided to test the effect of each of them inde-
pendently, similar to the study of Drakare et al. (2006) for z-values
and Deng ler et al. (2020a) for sh apes of SARs. From a s tatistical
point of view multiple regressions, which analyse a multitude of
predictors simultaneously, including potential interactions, might be
considered advantageous. However,inour case such an approach
would have drastically reduced the spatial coverage or forced us
to restrict ourselves to those variables that can be retrieved from
mined fine grain data. For the continuous variables(see Appendix
S3), we used bivariate linear regressions to test for their potential in-
fluence on the z-valuesofthethreetaxonomicgroupsandcomplete
removed thequadraticterm if non-significant.Toallow fortheas-
visualize d a polynomial su rface using l ocal fitt ing as implemen ted
in the R package stats by the ‘loess’ function (with smoothing pa-
rameter αsetto0.8). For categoricalpredictors(seeAppendixS3),
weappliedanalysisofvariance (ANOVA),followedbyTukey'spost
hoc test (R package stats) and multcompView (Graves et al., 2019) to
identify homogeneous groups. The comparison of taxonomic groups
all three t axonomic groups had been recorded simultaneously. In this
the R package lme4(version1.1-19;Batesetal.,2015)followedbya
Tukey's post hoc test as implemented in the function ‘glht’ of the R
package multcomp(version1.4-8;Hothornetal.,2008).
 7 of 15
Journal of Vegetation Science
The results obtained for S-spaceandlogS-spacewerequalitatively
similar; in log S-spaceonaveragethemodelledz-valueswereslightly
higher and R²adj about 25% higher than in S-space (for n, R²adj, pa-
rameter estimates and p-values in both S-spaces, see Appendix
S6). Thus (and to be consistent with Dengler et al., 2020a), we re-
port here the results in S-spaceindetail(results inlogS-spaceare
cular plants, for which we have the most comprehensive data set.
Generally, the results for bryophytes, lichens and complete vegeta-
tion were similar; thus, we mention them only when there were im-
portant deviations. As we tested numerous bivariate relationships
with large amounts of observations, the results of significance tests
should be viewed with caution. While we report all significant rela-
tionships in the Results, we focus the Discussion on those relation-
ships with a relevant amount of explanatory power (mostly R2
adj >
3.1 | Taxonomicgroups
The z-valuesofthetaxonomicgroupsdifferedsignificantly,whether
sets in which vascular plants, bryophytes and lichens were sampled
simultaneously(mixed-effectsmodelwith plot ID asarandomfac-
tor; Figure 2). The highest z-valu es across all dat a sets in S- s p a c e 
were found in lichens (mean ± standard deviation: 0.28 ± 0.14, me-
dian: 0.25), followed by vascular plants (0.23 ± 0.10, median: 0.21)
and bryophytes (0.19 ± 0.11, median: 0.17). The order was the same
groups had data, with lichens (0.29 ± 0.15, median: 0.26) followed
by vascular plants (0.22 ± 0.05, median: 0.21) and br yophytes
(0.20 ± 0.11, median: 0.18).
3.2 | Biogeographiccharacteristics
For vascular plants and bryophytes, z-valueshadaU-shaped,slightly
negative relationship with latitude and a positive relationship with
elevation (Figure 3 and Ap pendix S4). Fo r lichens, the r elationship
between z-valuesandelevation was slightlyhump-shaped,andthe
relationship with latitude was not significant (Appendix S4). For
complete vegetation, only latitude showed a significant relationship,
WefoundU-shapedrelationshipsformean annual temperature,
temperature seasonality and precipitation seasonality in the case of
z-values showed a U-shaped relationship only with temperature
seasona lity (Appe ndix S4). By contr ast, the z-values of complete
vegetation were negatively related to temperature seasonality
(Appe ndix S4). Onl y vascular pla nt z-values showedasignificant,
hump-shapedrelationshipwithmean annual precipitation (Figure 3,
3.3 | Ecologicalcharacteristics
For vascular plants and complete vegetation, z-values hadhump-
shaped relationships with soil pH (Figure 3, Appendix S4), while
bryophytes and lichens did not show a significant pattern with this
variable (Appendix S4). For vascularplants and complete vegeta-
tion, z-valueshadanegativeandinthelattercaseslightlyU-shaped
FIGURE2 Differences in z-values(modelledinS-space)amongthethreetaxonomicgroups:vascularplants,br yophytesandlichens,and
that the number of replicates in [b] is lower than for complete vegetation in [a] as in many plots bryophytes and lichens were considered, but
among groups (p <0.05)astestedwithaTukeyposthoctestwithANOVAfor(a)andalinearmixedeffectmodelwithplotseriesIDas
random effect (on intercept) for (b)
Vascular pl
(a) All plot series (b) Plot series with z in all groups
Vascular pl.
0.00.2 0.40.6 0.81.0
862 4554 716 400
349 349 349 349
8 of 15 
Journal of Vegetation Science DEMBIC Z Et al.
relationship with soil depth (Figure 3, Appendix S4). By contrast,
z-values of br yophytes and l ichens were not relat ed to soil depth
(AppendixS4). Fertilized grasslands had significantly lower z-values
in vascular plants than unfertilized ones (mean values: 0.15 vs 0.23;
low number of replicates). For vascular plant s, z-valueshadastrongly
decreasingandslightly U-shaped relationship with herb layer cover
(Figure 3), while the relationship was insignificant for bryophytes,
increasing for lichens and hump-shaped for complete vegetation
Acrossalltaxonomicgroups,z-valueswere positivelyrelatedto
slope inclination (Figure 3, App endix S4). However, the smo othed
curve for vascular plants shows that in the flattest areas (slope in-
clination <7°), the relationship was negative (Figure 3). The z-values
FIGURE3 z-Valuesforvascularplants(modelledinS-space)inrelationto:(a)latitude,(b)elevation,(c),meanannualtemperature,(d)
(j) litter cover, (k) soil depth CV, (l) microtopography, (m) rock and stone cover, (n) shrub layer cover, (o) herb layer cover, and (p) c-value(=
modelledrichnessat1m²,asmeasureofα-diversity).Redlinesindicatesignificantlinear,hump-shapedorU-shapedrelationships(p < 0.01)
with confidence intervals, while the blue lines represent local polynomial regression with confidence intervals
30 40 50 60 70
Latitude [°]
010002000 3000 4000
Elevation [m a.s.l.]
Mean annual temperature [°C]
500 1000
Mean annual precipitation [mm]
R< 0
Te mperature seasonality [°C]
30 60 90
Precipitation seasonality [%]
Soil pH
0255075 100
Soil depth mean [cm]
Slope inclination [°]
Litter cover [%]
050100 150
Soil depth CV [%]
R< 0
0255075 100
Microtopography [cm]
Rock and stone cover [%]
Shrub layer cover [%]
0255075 100
Herb layer cover [%]
 9 of 15
Journal of Vegetation Science
of vascular plants had a U-shaped relationship with litter cover,
with a strongly negative influence of this factor in the range from
0% to 20% indicated by the smoothing function (Figure 3). There
20% cover (A ppendix S4), whi le the relationshi p was positive for
lichens an d complete veget ation (App endix S4). Natural grasslands
had significantly higher z-values than secondary ones for vascular
plants a nd complete veget ation (Figure 4). M oreover, for vascular
plants, there was a strong and consistent decrease in z-valueswith
increasing land use intensity both within the natural and the sec-
ondar y grasslands (Appendix S4). The z-val ues of vascular plant s
were clearly influenced by livestock grazing and mowing, with the
highest valuesfoundin unusedgrasslands,followedbyonlygrazed
and only mown grasslands and finally those subject to both manage-
ment techniques (Figure 5). For the two other taxonomic groups and
the complete vegetation, the patterns were less pronounced, but
with a tendency toward higher z-values in grazed-only grasslands
(Figure 5). For burning, we did not find an effect on z-values,except
in bryophytes where unburned grasslands had significantly lower
Soil depth CV had a weak h ump-shaped ef fect for z-values of
vascular plants, but a positive one on those of complete vegetation
(Figure 3, Appendix S4). Microtopography was a positive predictor
for z-valuesofvascular plants and complete vegetation,whilefor
bryop hytes the rel ationship was sl ightly hump-s haped, and fo r li-
chens, it wasnon-significant(Figure3,Appendix S4). Forvascular
plants, bryophytes and complete vegetation, z-values increased
monotonically with rock and stone cover, while there was no rela-
bryophytes, z-values had a hump-shaped relationship with shrub
cover, while for lichens and complete vegetation it was positive
FIGURE4 Differences of z-
values (modelled in S-space)between
secondary and natural grasslands for
the four taxonomic groups: (a) complete
vegetation, (b) vascular plants, (c)
bryophytes, and (d) lichens. The numbers
plot series used, while different letters
indicate significant differences between
the groups
(a) Complete (b) Vascular (c) Bryophytes (d) Lichens
0.0 0.4 0.8
410 451
2926 1584
399 316 202 198
FIGURE5 Effectofgrazingand
mowing on z-values(modelledinS-
space) for the four taxonomic groups:
(a) complete vegetation, (b) vascular
plants, (c) bryophytes, and (d) lichens. The
figures on top indicate the numbers of
letters indicate significant differences
among groups according to the post
hoc test
and mown
only grazed
only mown
d and mown
only grazed
only mown
and mown
only grazed
only mown
and mown
only grazed
only mown
(a) Complete
(b) Vascular
plants (c) Bryophytes (d) Lichens
0.0 0.4 0.8
19384 259 144
ab acbc
3091752 1657 599
17281 244 138
171 159 29 0
10 of 15 
Journal of Vegetation Science DEMBIC Z Et al.
3.4 |α- diversity
Thez-valuesexhibitedastrongrelationshipwiththec- values of the
power model,i.e.themodelledrichnessat1m².Forvascular plants,
bryophytesandlichens individually,the relationshipwasU-shaped
with minima around 20 species for vascular plants and about 10 spe-
contrast, for complete vegetation, the relationship was linear nega-
3.5 | Explanatorypowerofthedifferentpredictors
Overall, the explanatory power of the bivariate models was rela-
tively low, with R2
adj ranging from <0.01to0.41(AppendixS6).The
mean predictive power of the 16 bivariate regressions was 0.07 for
vascular plants, 0.02 for bryophytes, 0.02 for lichens and 0.03 for
variance of z-values of vascular plants was found for herb layer
cover (R2
adj = 0.41), followed by naturalness at the fine level (0.18),
elevation(0.15),rockand stonecoveraswell as grazingand mow-
ing (both 0.14) and the c-value(0.11).Thevariablewiththehighest
adj value for bryophyte and lichen z-valueswasthec-value(R2
= 0.08 and 0.16, respectively), while all other predictors had R2
adj <
adj for complete
vegetation were soil depth CV (R2
adj = 0.10), followed by inclination
4.1 | Explanatorypower
Althoughmanyofthetested variables, representingbothbiogeo-
graphical and local habitat characteristics, were significant, the ex-
planatory power of these bivariate models was low, with only few
variables exceeding 10% explained variance. This is in striking con-
which often find R²adj values above 50% with only one or a few
predictors(Pinto-Ledezmaetal.,2018).There areonlyfew large-
extent,fine-grainstudies inmacroecology(Becketal.,2012),and
thus few examples of how much explained variance one can expect.
Bruelheideetal.(2018),inaglobal studyofcommunity-weighted
means of traits, found that none of 30 tested environmental vari-
ables explained more than 10% of the total variance, and all 30
together only 10.8%. Reasons for the relatively low explained
variance in fine-grain macroecological studies include the possi-
ble effects of other unmeasured factors, such as legacy effects,
influences of the surroundings, and interspecific interactions, and
a spatial mismatch between the environmental predictors (mostly
derived from coarse- or at bes t medium-grain global databases)
analyses based on GrassPlot have the advantage that, unlike those
in Bruelheide et al. (2018; based on sPlot), they contain numerous
well-curated in-situ determined predictor variables (soil, micro-
topography, heterogeneity, land use, vegetation structure), which
coincides with the relatively higher explained variance in our case.
However,forclim at icvariables,wealsoha dtorelyoncoarser-gr ain
data, despite it being known that temperature can strongly vary
across short distances, particularly in mountains (Opedal et al.,
different aspects, including many that typically yield high explan-
atory power for different facets of biodiversity, both in classical
macroecological (large extent, coarse grain) and vegetation ecologi-
cal (small extent, fine grain) studies, we doubt that other variables
individually would yield much higher R²values.Rather,weassume
that relatively low explained variance will be a typical outcome of
4.2 | Mechanismsdrivingvariationinz- values
The relationships between β-divers ity and a wide r ange of pre-
fluence of these variables on mean occupancy, which determines
β-diversity (Storch, 2016). At fine spatial scales one candecom-
pose the spatial arrangement of plant communities into three dif-
ferent aspects that together make up mean occupancy: (a) total
cover; (b) mean size of individuals; and (c) similarit y of species
composition between adjacent subplots. While the relationships
between these three aspects and mean occupancy are mathemati-
cally self-evident (rightpart ofFigure 6,Appendix S7),the open
question prior to our study was how various environmental driv-
ers or species properties would influence one or several of these
aspects. Inspired by our findings and theoretical considerations,
we have developed a conceptual model (Figure 6), which is able to
explain some surprising outcomes of our study. For example, vari-
ables could have no or very weak effects when positive and nega-
tive influences on mean occupancy cancel themselves out, while
some “aggregated” variables could have unexpectedly strong ef-
fects when they influence mean occupancy consistently via more
than one pathway. While the left and middle parts of Figure 6 are
consistent with our findings, they should be seen as a set of test-
able hypotheses. In the following we will discuss our individual
findings in this framework.
4.3 | Taxonomicgroups
The z-values differed significantly among taxonomic groups (li-
chens > vascular plants >bryophytes).Astudyatmuchcoarser
grain sizes (regional to continental) by Patiño et al.(2014) found
similar z-valuesof0.18and0.21forthet wolineagesofbr yophytes
(i.e. liverworts and mosses) and 0.21 and 0.33 for the two lineages
of vascular plants (i.e. pteridophytes and spermatophytes). Patiño
etal.(2014)attributed theflatterSARsof liver worts,mossesand
 11 of 15
Journal of Vegetation Science
pteridophytes to their higher long-distance dispersal capabilities
via spores compared to spermatophytes via seeds or other much
heavier diaspores. Dispersal limitation might also play a role at
short distances, particularly when considering that the majority
ofvascular plants are spreading clonally.Whileour small-grainz-
values forbryophytes(0.19)weresimilarto the coarse-grain val-
ues of Patiño et al. (2014), those for vascular plants (0.23) were
much lower than their coarse-grain results for spermatophytes
(the dominant group of vascular plants: 0.33). We are not aware
of any coarse -grain study of S ARs of lichens, b ut since they are
also mainly distributed via spores or small vegetative diaspores,
one should assume low z-valuessimilartothoseofbryophytesand
mostly restricted to a few microhabitats with reduced competition
by vascular plants and bryophytes, typically around rock outcrops
or on shallow, open soil (i.e. in patches with strong abiotic stress). In
such microhabitats, not only one but a whole array of lichen species
can occur,leading toa steep SAR (i.e. highz-value).Wethushy-
of species groups are their mean dispersal distance and their mean
4.4 | Biogeographiccharacteristics
Among the climate variables, mean annual temperature had the
strongest influence on z-valueswithaU-shapedrelationship.This
could indicate that environmental stress leads to higher z-values.
the stress, while at the high end drought effects might be the stress
factor. By contrast, z-values showed only very weakrelationships
with the other three climatic factors, which highlights that there
grain z-values.
TheminimaoftheU-shapedrelationships ofz-valuesofvascu-
lar plants, bryophytes and complete vegetation with latitude were
around 5055°N. This finding differs substantially from the strong
negative relationship known for coarse-grain β-diversity in plants
(Qian & Ricklefs, 2007; Qian, 2009) as well as across taxa and scales
(meta-analysis by Drakare et al., 2006). Qiao et al. (2012), using
nested plots from forests in China, found a negative relationship be-
tween z-valuesandlatitudeforallvascularplants,treesandshrubs,
but not for herbaceous plants. The difference between our results
and the two studies (Drakare et al., 2006; Qiao et al. 2012) could
stem from the different ranges in latitude (Drakare et al., 2006:
FIGURE6 Conceptualfiguresummarizingourhypotheseshowdifferentdriverscouldinfluencefine-grainβ-diversityviachanging
and (iii) similarity of species composition between adjacent subplots. These three aspects of mean occupancy again are affected by the
environmental drivers: productivity, stress, disturbances as well as heterogeneity (green). Note that disturbance can have contrasting
effects depending on its type and intensity. To the very left we exemplify how two aggregated environmental parameters, land use
make up mean occupancy is illustrated with a pair of figures showing to the left a situation with low and to the right with high value of the
respective aspect. The four different symbols represent individuals of four species distributed in a vegetation plot of a total extent of Aγ
=9andassessedalsoatagrainsizeofAα = 1. Influences of one parameter are indicated by the arrows with their + and – symbols, with
grey arrows corresponding to ecological hypotheses and black arrows to strict mathematical relationships. We did not aim to display all
possible relationships in this figure, but concentrated on those that we consider most important. The expected effect of a certain driver
12 of 15 
Journal of Vegetation Science DEMBIC Z Et al.
0– 60°; Qiao et al., 2012: 19– 52° vs 35– 70° in the present study).
The poleward decrease until ca. 50– 55° is consistent across all three
missed by the other studies because their gradients did not extend
so farpoleward. Moreover, specifically for grasslands, higher land
N) could have contributed to the reduced z-valuesthere(seebelow).
and bryophyteswith elevation,which contrasts withMoradietal.
(2020) for grasslands in Iran (2,00 0– 4,500 m a.s.l.), Kraft et al. (2011)
for forests in Ecuador (400– 2300 m a.s.l.; only trees) and Qiao et al.
(2012) for forests in China (300– 3,150 m a.s.l.), who found decreas-
ing z-values.However,itisinagreementwith findingsforz-values
der Mer we & van Rooyen, 2011). We assume th at the increasing
fine-grainβ-diversitywithelevationcan be explained by (a)the in-
creased harshness of the climate with increasing elevation and re-
sulting stress for plants, possibly impacting spatial patterns of plants
(see above for latitude); (b) an increased role of facilitation leading
alsotoclustereddistributionsofspecies (Anthelmeetal.,2014);(c)
higher species turnover at small distances in an increasingly rugged
topogra phy and thus stro nger small-scal e gradients of soi l condi-
tions, water availability and microclimate, which are generally much
more pronounced at higher elevations (Körner, 2003); and (d) as for
latitude, the natural patterns possibly being amplified by higher land
use intensities at lower elevations.
4.5 | Stress–productivity
For vascular plants, the relationship with fertilization, soil depth
grain β-diversitywithhigherproductivity.Adecreaseinβ-diversity
means an increase in mean occupancy (Storch, 2016; Figure 6), which
can happen either if all species become more frequent or if the rar-
est species are dropped out from the community due to asymmet-
riccompetition.Indeed,Filibeck et al.(2019)found thatfine-grain
z-values in Italian limestonegrasslands were negativelycorrelated
with soil d epth, as deep-so il sites were colonized by co mpetitive
and patch-forming species, curtailing composition heterogeneity.
In addition, Chiarucci et al. (2006) found a negative relationship be-
tween z-valuesandgrasslandproductivityinItalyandGermany.By
contrast,DeMalachetal.(2019),studyingdrylandsworldwide with
a different measure of β-diversity, foundthe opposite pattern, i.e.
increasing β-diversity withhighercover.Thisdiscrepancyishardto
explain, but our data set is much more comprehensive in environ-
mental space and numbers; thus we trust that our findings are more
general. Finally, we only found a minimal effect of productivity-
related predictors on the z-valuesofbryophytesandlichens.Apos-
sible explanation could be that the direct effects of productivity
are counteracted by the opposing effects of increased herb layer
cover, which increases the stress for bryophytes through lower light
4.6 | Disturbance
Wefound thatnaturalgrasslands hadhigherfine-grain β-diversity
than secondary grasslands whose existence depends on anthropo-
affected z-values negatively, but more strongly for mowing. Thus,
β-diversity in open vegetation. It is underst andable that mowing
particularly strongly decreases z-valuesasitremovesabove-ground
biomass non-selectively, thus reduces interspecific competition
(Wilson et al., 2012), thereby increasing stand homogeneity. Besides
ac tualdi stur bancee ffec t s,live s toc kgr azi ngc anc rea tesomehetero -
geneity in comparison to meadows, e.g. due to selective feeding, the
heterogeneous trampling intensity and patchy distribution of excre-
ters yielded R²adj values of up to 0.20, the explained variances of our
two other measures related to disturbance, slope inclination and lit-
ter cover were 0.03 or less, indicating that agricultural disturbances
have a different influence on z-valuesthanabioticdisturbances.
4.7 | Heterogeneity
z-v aluestobeass oci atedwit hhighsoildepthC V,highmicroto pog ra-
phy, intermedia te rock and stone cover an d intermediate shr ub cover.
variances of 0.02 or less, which contrasts with some geographically
et al., 2016). Only rock and stone cover had a moderate effect in the
case of vascular plants (R²adj = 0.14) and complete vegetation (0.05),
but, contrary to our assumption, we found the highest z-valuesat
close to 100% rocks and stones. This is logical due to the negative
relationshipbetweenz-valuesand meanoccupancy:theless space
is available for plants to grow inside a plot, the lower the mean oc-
4.8 |α- Diversity
The z-valuesshowedanunexpectedU-shapedrelationshipwiththe
the power function represents the intercept in the log– log repre-
sentation or, in other words, the species richness at the unit area (in
richness of whole plots (“γ-diversity”) wasconstant,ahighervalue
of the slope parameter would necessarily lead to a lower intercept,
so that the relationship between z and c would be negative. Since
patternsare possible.While moderately species-rich plots located
in suboptimal/stressful conditions indeed had the expected nega-
tive relationship between z and c, there were some plots character-
izedbyboth highcand z, which meansthat these plotsmust also
 13 of 15
Journal of Vegetation Science
have exceptionally high total richness. This indicates that the most
species-richplotsarecharacterizedby aprevalenceofsubordinate
species with low mean occupancy. Our finding contrasts with the
strong negative relationships between z and c recently reported
for island SARs of archipelagos across the globe (Matthews et al.,
2019b), where γ-diversity also varied substantially.The reason for
the discrepancy is unknown, but it could be related to the differ-
4.9 | Conclusionsandoutlook
While, before our study, there was only scattered and inconclusive
our comprehensive study has now enabled us to propose a theory
consisting of a set of hypotheses that are in agreement with our
hypotheses should be tested with observational or, even better, ex-
perimental studies. While our findings partly concur with those from
declines with elevation (Tello et al., 2015; Sabatini et al., 2018) and
latitude(Qian &Ricklefs, 2007; Qian, 2009), fine-grainβ-diversity
lationship with latitude. Similar scale dependence of drivers is well
known for α-diversity(Fieldetal.,2009;Siefertetal.,2012).Itwill
Weth anka llvege t ati o nsc ient istsw hoc a refu llycoll e cted them ulti-scale
plant diversity data from Palaearctic grasslands available in GrassPlot.
The Eurasian Dry Grassland Group (EDGG) and the International
Associ ation for Vegetatio n Science (IAVS) suppo rted the EDGG F ield
Workshops, which generated a core part of the GrassPlot data.
JDe conceived the idea of this paper andinitiatedthe datacollec-
conducted the statistical analyses, ID and JDe led the writing. All
authors checked, improved and approved the manuscript.
The data used in this paper are derived from the collaborative
vegetatio n-plot database G rassPlot (Dengle r et al., 2018; Biurrun
et al., 2019), version 2.02. They can be requested from GrassPlot
with a project proposal following the GrassPlot Bylaws (see https:// ases/Grass Plot).
Iwona Dembicz
Jürgen Dengler
Manuel J. Steinbauer
Thomas J. Matthews
Sándor Bartha
Sabina Burrascano
Alessandro Chiarucci
Goffredo Filibeck
François Gillet
Monika Janišová
Salza Palpurina
David Storch
Werner Ulrich
Svetlana Aćić
Steffen Boch
Juan Antonio Campos
Laura Cancellieri
Marta Carboni
Giampiero Ciaschetti
Timo Conradi
Pieter De Frenne
Jiri Dolezal
Franz Essl
Edy Fantinato
Itziar García- Mijangos
Gian Pietro Giusso del Galdo https://orcid.
John- Arvid Grytnes
Riccardo Guarino
Behlül Güler
Jutta Kapfer
Łukasz Kozub
Anna Kuzemko
Michael Manthey
Corrado Marcenò
Anne Mimet
Alireza Naqinezhad
Jalil Noroozi
Arkadiusz Nowak
Harald Pauli
Robert K . Peet
Vincent Pellissier
Remigiusz Pielech
Ma ssi mo Te rzi
Emin Uğurlu
Orsol ya Val
Iuliia Vasheniak
Kiril Vassilev
Denys Vynokurov
Hannah J. White
Wolfgang Willner
Manuela Winkler
Sebastian Wolfrum
Jinghui Zhang
Idoia Biurrun
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Additional supporting information may be found online in the
Supporting Information section.
AppendixS2. Additionalinformation on theoriginofthe analysed
Appendix S3. Detailed overview of the considered predictor
AppendixS4.AdditionalresultsinS- s p a c e
AppendixS5.Analysesofz-valuesmodelledinlogS - s p a c e
AppendixS6. The numeric outputs of all analyses in S-space and
log S - s p a c e
Appendix S7. Conceptual figure illustrating how different drivers
How to cite this article:DembiczI,DenglerJ,SteinbauerMJ,
vegetation. J Veg Sci. 2021;32:e13045. ht t p s : //do i .
org /10.1111/jvs.13045
... EDITORIAL fine-grain plant community data to address macroecological questions at various extents: global (Kusumoto et al., 2021;Testolin et al., 2021), across the whole Palaearctic Dembicz et al., 2021;Zhang et al., 2021), across Europe Boonman et al., 2021;Padullés Cubino et al., 2021;Sporbert et al., 2021;Večera et al., 2021), larger parts of Europe (Cao Pinna et al., 2021;Wagner et al., 2021) or at state level (Bourgeois et al., 2021;Craven et al., 2021). Most of these studies rely on two large vegetation-plot databases established and maintained by two working groups of the International Association for Vegetation Science (IAVS), the European Vegetation Archive (EVA; Chytrý et al., 2016) by the European Vegetation Survey Boonman et al., 2021;Cao Pinna et al., 2021;Padullés Cubino et al., 2021;Sporbert et al., 2021;Večera et al., 2021;Wagner et al., 2021) and the GrassPlot database by the Eurasian Dry Grassland Group Dembicz et al., 2021;Zhang et al., 2021). ...
... EDITORIAL fine-grain plant community data to address macroecological questions at various extents: global (Kusumoto et al., 2021;Testolin et al., 2021), across the whole Palaearctic Dembicz et al., 2021;Zhang et al., 2021), across Europe Boonman et al., 2021;Padullés Cubino et al., 2021;Sporbert et al., 2021;Večera et al., 2021), larger parts of Europe (Cao Pinna et al., 2021;Wagner et al., 2021) or at state level (Bourgeois et al., 2021;Craven et al., 2021). Most of these studies rely on two large vegetation-plot databases established and maintained by two working groups of the International Association for Vegetation Science (IAVS), the European Vegetation Archive (EVA; Chytrý et al., 2016) by the European Vegetation Survey Boonman et al., 2021;Cao Pinna et al., 2021;Padullés Cubino et al., 2021;Sporbert et al., 2021;Večera et al., 2021;Wagner et al., 2021) and the GrassPlot database by the Eurasian Dry Grassland Group Dembicz et al., 2021;Zhang et al., 2021). Testolin et al. (2021) used data from the global vegetation-plot database sPlot (Bruelheide et al., 2019), and four relied on regional data compilations (Bourgeois et al., 2021;Craven et al., 2021;Kusumoto et al., 2021;Tordoni et al., 2021). ...
... The fractions of explained variance in statistical models were generally much lower than 'usual' in coarse-grain macroecological studies, where often two or three predictors are enough to reach an R 2 of more than 50%. For example, Dembicz et al. (2021) found a mean R 2 for single predictors of fine-grain beta diversity of vascular plants of 7%, and Wagner et al. (2021) could only explain 21% of the variation in alien species covers, even with a multiple regression with 13 predictors. These results are in line with other fine-grained studies at large extents (e.g., Bruelheide et al., 2018). ...
... At the fine-grained spatial scales of our study, beta diversity tends to be high due to geometric reasons related to mean occupancy of species in samples (Storch, 2016). In a study in grassland plots of sizes comparable to those of our study, Dembicz et al. (2021) analyzed the z coefficient of the power law species-area relationship, a parameter that is a measure of beta diversity (Koleff et al., 2003). They showed that factors that affect plant cover and/or number of individuals have direct effects on beta diversity by increasing or decreasing the number of subplots occupied by individual species, thus increasing or decreasing similarity in species composition. ...
... Although this is a measure of pairwise dissimilarity, which does not reflect total heterogeneity in the pool of sampling quadrats (Baselga, 2013), it clearly points to a generalized homogenization of adjacent pairs of sites. A likely explanation is an increase in the abundance of individuals (indicated by the increase in herbaceous cover), which would increase the shared presences of species in adjacent pairs of plots (Dembicz et al., 2021;Storch, 2016). This applied to the full set of species and to the groups of species with different dispersal abilities, but the decrease in dissimilarity was much stronger in species with anemochory, suggesting that although these species have the potential to disperse long distances, short-distance dispersal was dominant (Cousens et al., 2008;Plue & Hermy, 2012). ...
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Abstract Beta diversity, and its components of turnover and nestedness, reflects the processes governing community assembly, such as dispersal limitation or biotic interactions, but it is unclear how they operate at the local scale and how their role changes along postfire succession. Here, we analyzed the patterns of beta diversity and its components in a herbaceous plant community after fire, and in relation to dispersal ability, in Central Spain. We calculated multiple‐site beta diversity (βSOR) and its components of turnover (βSIM) and nestedness (βSNE) of all herbaceous plants, or grouped by dispersal syndrome (autochory, anemochory, and zoochory), during the first 3 years after wildfire. We evaluated the relationship between pairwise beta diversity (βsor), and its components (βsim, βsne), and spatial distance or differences in woody plant cover, a proxy of biotic interactions. We found high multiple‐site beta diversity dominated by the turnover component. Community dissimilarity increased with spatial distance, driven mostly by the turnover component. Species with less dispersal ability (i.e., autochory) showed a stronger spatial pattern of dissimilarity. Biotic interactions with woody plants contributed less to community dissimilarity, which tended to occur through the nestedness component. These results suggest that dispersal limitation prevails over biotic interactions with woody plants as a driver of local community assembly, even for species with high dispersal ability. These results contribute to our understanding of postfire community assembly and vegetation dynamics.
... Recently it has been shown with an extensive dataset of nested-plot data from open vegetation types across the Palaearctic biogeographic realm that this function is generally the best SAR model also at fine grains in continuous habitats (Dengler et al. 2020a). The exponent z can then be used as a valid measure of β-diversity (Koleff et al. 2003;Jurasinski et al. 2009;Polyakova et al. 2016;Dembicz et al. 2021b). However, previous studies did not yield consistent results on the drivers of z values or the question whether z values are completely scale-invariant. ...
... Finally, when analysing the scale dependence of z values, we found a slight peak for the transition from 0.01 to 0.1 m 2 with decreases towards both the smaller and the larger grain sizes. These findings correspond to those in regional studies of Turtureanu et al. (2014), Polyakova et al. (2016) and Talebi et al. (2021), while other studies did not find a scale dependence of z values (Kuzemko et al. 2016;Dembicz et al. 2021b). This demonstrates that the scale dependence of z values in dry grasslands of the Palaearctic is generally weak, but if there is one, it always exhibits a peak at grain sizes clearly below 1 m 2 , pointing to a very fine-grained community organisation. ...
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The inneralpine dry valleys of the Swiss Alps are characterized by subcontinental climate, leading to many peculiarities in dry grassland species composition. Despite their well-known uniqueness, comprehensive studies on biodiversity patterns of the dry grasslands in these valleys were still missing. To close this gap, we sampled 161 10-m² vegetation plots in the Rhône, Rhine and Inn valleys, recording vascular plants, terricolous bryophyte and lichen species, as well as environmental data. Additionally, we tested the scale-dependence of environmental drivers using 34 nested-plot series with seven grain sizes (0.0001–100 m2). We analysed the effects of environmental drivers related to productivity/stress, disturbance and within-plot heterogeneity on species richness. Mean species richness ranged from 2.3 species in 0.0001 m2 to 58.8 species in 100 m2. For all taxa combined, the most relevant drivers at the grain size of 10 m2 were southing (negative), litter (negative), mean annual precipitation (unimodal), gravel cover (negative), inclination (unimodal) and mean annual precipitation (unimodal). For vascular plants the pattern was similar, while bryophyte and lichen richness differed by the opposite relationship to mean annual precipitation as well as negative influences of mean herb layer height, grazing and mowing. The explained variance of the multiple regression model increased with grain size, with very low values for the smallest two grain sizes. While southing and litter had high importance for the fiver larger grain sizes, pH and gravel cover were particularly important at the intermediate grain sizes, and inclination and mean annual precipitation for the two largest grain sizes. The findings emphasize the importance of taxonomic group and grain size for patterns and drivers of species richness in vegetation, consistent with ecological theory. Differences in the diversity-environment relationships among the three taxonomic groups can partly be explained by asymmetric competition that leads to low bryophyte and lichen diversity where vascular plants do well and vice versa. The relatively low alpha diversity of vascular plants in dry grasslands in Swiss inneralpine valleys compared to similar communities in other parts of the Palaearctic remains puzzling, especially because Swiss stands are often large and well-preserved.
... However, the temporal behavior of the z parameter has not been explored. In a macroecological survey, the z parameter was variable among sites, and a larger z parameter was found in grasslands exposed to high environmental stress or disturbances [59]. Consequently, we might expect an increase in the z parameter during extreme droughts. ...
Full-text available
Diversity responses to climatic factors in plant communities are well understood from experiments, but less known in natural conditions due to the rarity of appropriate long-term observational data. In this paper, we use long-term transect data sampled annually in three natural grasslands of different species pools, soils, landscape contexts and land use histories. Analyzing these specific belt transect data of contiguous small sampling units enabled us to explore scale dependence and spatial synchrony of diversity patterns within and among sites. The 14-year study period covered several droughts, including one extreme event between 2011 and 2012. We demonstrated that all natural grasslands responded to droughts by considerable fluctuations of diversity, but, overall, they remained stable. The plant functional group of annuals showed high resilience at all sites, while perennials were resistant to droughts. Our results were robust to changing spatial scales of observations, and we also demonstrated that within-site spatial synchrony could be used as a sensitive indicator of external climatic effects. We propose the broad application of high-resolution belt transects for powerful and adaptive vegetation monitoring in the future.
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With this editorial, we introduce the Special Collection “Classification of grasslands and other open vegetation types in the Palaearctic”. In searching the Web of Science for classification papers on Palaearctic grasslands, we found 207 studies from 1972–2021, including 106 typical classification works. These studies originated mainly from Europe, with only few from Asia and only one from Northern Africa. While Europe in the 20 th century already had a strong tradition in regional classification studies, the launch of a common plot database (European Vegetation Archive, EVA) and a continental syntaxonomic reference list (EuroVegChecklist) have spurred the developments there in recent years. We then introduce the seven articles of the Special Collection. Four of them present regional studies of certain vegetation types, namely spring vegetation ( Montio-Cardaminetea ) in Grisons, Switzerland, dry grasslands ( Festuco-Brometea ) of the inneralpine valleys of Austria, montane to subalpine tall-herb vegetation ( Mulgedio-Aconitetea ) in the Sudetes Mts., Poland, and steppe depressions ( Festuco-Brometea and Molinio-Arrhentatheretea ) in Southern Ukraine. A new synthesis of the grassland vegetation of Navarre in Spain (all classes, focus on Festuco-Brometea ), started with an unsupervised classification and translated it into a hierarchical expert system, while another study provided the first synthesis of the tall-herb vegetation (mainly Ulopteretea prangae ) of Tajikistan. Finally, a study based on the GrassPlot database compared fine-grain beta-diversities across open vegetation types of the Palaearctic. Abbreviations : EDGG = Eurasian Dry Grassland Group, EVA = European Vegetation Archive, IAVS = International Association for Vegetation Science, WoS = Web of Science.
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The 15th EDGG Field Workshop took place from 24 May to 3 June 2021 in Southern Ukraine (Kherson and Mykolaiv administrative regions). Over 10 days, we sampled different types of grasslands, mainly focusing on dry grasslands of the classes Festuco-Brometea, Koelerio-Corynephoretea canescentis, and Festuco-Puccinellietea (steppic, sandy and saline, respectively) but also taking into account other open habitats, such as mesic grasslands and dunes. In total, we sampled 50 nested-plot series with 7-8 grain sizes from 1 cm2 to 100 m2 and, in some cases, up to 1000 m2 ("EDGG Biodiversity Plots"), plus 74 additional normal plots of 10 m2. We comprehensively sampled vascular plants as well as terricolous bryophytes and lichens, and, for the first time also Sciaridae (Diptera, Insecta). One vascular plant species (Torilis pseudonodosa), as well as two lichen species (Cladonia conista and Endocarpon loscosii), were recorded for the first time from Ukraine. Two species of moss (Rhynchostegium megapolitanum and Ptychostomum torquescens) and three species of lichen (Cladonia cervicornis, C. symphycarpa, and Involucropyrenium breussi) were reported for the first time for the Kherson region. We summarize the scale-dependent richness values and compare them with those from other studies. The report concludes with a photo diary with impressions from the Field Workshop.
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We propose an equation to evaluate the efficiency of a classification as a function of the effort required and the population size of data collectors. The formula postulates a "classification efficiency coefficient", which relates not only to the complexity of the object to be classified, but also to the data availability and representativeness. When applied to the classification of phytocoenoses, the equation suggests that a classification system based on vascular plants offers the best compromise between sampling effort, resolution power and data availability. We discuss the possibility of basing a vegetation classification on plot records for all macroscopic photoautotrophic organisms co-occurring in the vertical projection of a given ground area, as recently suggested by some authors. We argue that the inclusion of cryptogams in the description of phytocoenoses dominated by vascular plants should rely on a synusial approach, conceived as complementary to the traditional Braun-Blanquet approach. Syntaxonomic reference: Mucina et al (2016).
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We report on the completed second volume of Vegetation Classification and Survey (VCS), whose content grew by 41% compared to the first volume. We were able to diversify article types, geographic coverage, authors and editorial team, the latter now consisting of 62 researchers from 29 countries with a female ratio of 31%. Three newly started Special Collections focus on the vegetation of the most diverse continents, which are at the same time least represented in the international literature: Africa, Asia and Latin America. We highlight six outstanding papers of the previous year, among them Dembicz et al. (2021b, Vegetation Classification and Survey 2: 293–304), which received the Editors’ Award 2021. In conclusion, we see a good perspective for the journal development and its inclusion in the leading citation databases, but the success strongly depends on authors and readers of VCS.
Aims : To quantify how fine-grain (within-plot) beta diversity differs among biomes and vegetation types. Study area : Palaearctic biogeographic realm. Methods : We extracted 4,654 nested-plot series with at least four different grain sizes between 0.0001 m² and 1,024 m² from the GrassPlot database spanning broad geographic and ecological gradients. Next, we calculated the slope parameter ( z -value) of the power-law species–area relationship (SAR) to use as a measure of multiplicative beta diversity. We did this separately for vascular plants, bryophytes and lichens and for the three groups combined (complete vegetation). We then tested whether z -values differed between biomes, ecological-physiognomic vegetation types at coarse and fine levels and phytosociological classes. Results : We found that z -values varied significantly among biomes and vegetation types. The explanatory power of area for species richness was highest for vascular plants, followed by complete vegetation, bryophytes and lichens. Within each species group, the explained variance increased with typological resolution. In vascular plants, adjusted R ² was 0.14 for biomes, but reached 0.50 for phytosociological classes. Among the biomes, mean z -values were particularly high in the Subtropics with winter rain (Mediterranean biome) and the Dry tropics and subtropics. Natural grasslands had higher z -values than secondary grasslands. Alpine and Mediterranean vegetation types had particularly high z -values whereas managed grasslands with benign soil and climate conditions and saline communities were characterised by particularly low z -values. Conclusions : In this study relating fine-grain beta diversity to typological units, we found distinct patterns. As we explain in a conceptual figure, these can be related to ultimate drivers, such as productivity, stress and disturbance, which can influence z -values via multiple pathways. The provided means, medians and quantiles of z -values for a wide range of typological entities provide benchmarks for local to continental studies, while calling for additional data from under-represented units. Syntaxonomic references : Mucina et al. (2016) for classes occurring in Europe; Ermakov (2012) for classes restricted to Asia. Abbreviations : ANOVA = analysis of variance; EDGG = Eurasian Dry Grassland Group; SAR = species-area relationship.
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Species–area relationships (SARs) are among the best investigated patterns in ecology, yet the shape of the function that should describe SARs and the biological meaning of the function parameters are disputed. Elevational gradients offer the opportunity of investigating how biodiversity responds to large variations in environmental characteristics within small geographical areas. We asked which function describes SARs at different elevations and explored how variations in environmental characteristics influence SAR shape. Alborz Mountains (Iran). Vascular plants. We used sets of nested plots (0.001 to 100 m2) placed at 100 m intervals from 2,000 to 4,500 m elevation to construct series of nested SARs as species accumulation curves. Then, we used these curves to assess the appropriateness of different SAR functions at different elevations. We investigated how parameters of the power function varied along the elevational gradient in response to variation in environmental parameters (ruggedness, temperature, precipitation, exposed rock, percentages of soil sand and total nitrogen, and productivity, expressed by the normalized difference vegetation index). The most frequently observed best fit model was the power function, which is controlled by two parameters: z (the velocity in species accumulation with sampled area) and c (the species richness per unit area). z was positively influenced by temperature and soil nitrogen, decreasing with elevation. c was positively influenced by temperature and soil nitrogen, and negatively by rock cover, decreasing with elevation. The decrease in c‐values with elevation is consistent with the altitudinal decrease in species richness and is explained by the increase in bare rock. By contrast, c was positively influenced by temperature and total nitrogen, which are two factors promoting plant growth. Similarly, z‐values decreased with elevation, thus indicating a decrease in beta diversity.
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GrassPlot is a collaborative vegetation-plot database organised by the Eurasian Dry Grassland Group (EDGG) and listed in the Global Index of Vegetation-Plot Databases (GIVD ID EU-00-003). Following a previous Long Database Report (Dengler et al. 2018, Phytocoenologia 48, 331–347), we provide here the first update on content and functionality of GrassPlot. The current version (GrassPlot v. 2.00) contains a total of 190,673 plots of different grain sizes across 28,171 independent plots, with 4,654 nested-plot series including at least four grain sizes. The database has improved its content as well as its functionality, including addition and harmonization of header data (land use, information on nestedness, structure and ecology) and preparation of species composition data. Currently, GrassPlot data are intensively used for broad-scale analyses of different aspects of alpha and beta diversity in grassland ecosystems.
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Aim: Species-area relationships (SARs) are fundamental scaling laws in ecology although their shape is still disputed. At larger areas power laws best represent SARs. Yet, it remains unclear whether SARs follow other shapes at finer spatial grains in continuous vegetation. We asked which function describes SARs best at small grains and explored how sampling methodology or the environment influence SAR shape. Location: Palaearctic grasslands and other non-forested habitats. Taxa: Vascular plants, bryophytes and lichens. Methods: We used the GrassPlot database, containing standardised vegetation-plot data from vascular plants, bryophytes, and lichens spanning a wide range of grassland types throughout the Palaearctic and including 2057 nested-plot series with at least seven grain sizes ranging from 1 cm2 to 1024 m². Using non-linear regression, we assessed the appropriateness of different SAR functions (power, power quadratic, power breakpoint, logarithmic, Michaelis-Menten). Based on AICc, we tested whether the ranking of functions differed among taxa, methodological settings, biomes or vegetation types. Results: The power function was the most suitable function across the studied taxonomic groups. The superiority of this function increased from lichens to bryophytes to vascular plants to all three taxonomic groups together. The sampling method was highly influential as rooted-presence sampling decreased the performance of the power function. By contrast, biome and vegetation type had practically no influence on the superiority of the power law. Main conclusions: We conclude that SARs of sessile organisms at smaller spatial grains are best approximated by a power function. This coincides with several other comprehensive studies of SARs at different grain sizes and for different taxa, thus supporting the general appropriateness of the power function for modelling species diversity over a wide range of grain sizes. The poor performance of the Michaelis-Menten function demonstrates that richness within plant communities generally does not approach any saturation, thus calling into question the concept of minimal area.
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Secondary dry grasslands in Europe can host high levels of vascular plant richness at small spatial scales. However, in Southern Europe their biodiversity patterns are largely unexplored. In this work, we aim at: (i) estimating plant species richness patterns at very fine scales in montane dry grasslands, on limestone bedrock, in Abruzzo Lazio and Molise National Park (Central Apennines, Italy); (ii) assessing the most important physical and edaphic drivers of biodiversity patterns at multiple plot sizes. We used randomly placed nested-plot series where we measured alpha-diversity at three different plot sizes (1 m2, 0.1 m2 and 0.01 m2) and within-plot beta-diversity (as expressed by the slope of the species-area curve across plot sizes). Variable selection was performed by means of Random Forests. Relationships between selected variables and diversity measures were then assessed using Regression Trees, Linear and Generalized Linear Models. Overall, results pointed to topographically-controlled edaphic factors (soil pH and silt fraction) as the main drivers positively influencing alpha-diversity at all spatial scales, with a positive effect of rock cover and slope inclination at smaller spatial grains. Beta-diversity was positively influenced by rock cover. We suggest that high-pH, steep and/or rocky sites feature higher species richness because they lack competitive grass species. Our results are in agreement with previous works underlining the importance of less productive habitats for the conservation of secondary grassland biodiversity.