species richness, community specialization and soil-vegetation relationships of managed grasslands...

Species Richness, Community Specializationand Soil-Vegetation Relationships of ManagedGrasslands in a Geologically Heterogeneous Landscape

Lucia Cachovanová & Michal Hájek &

Zuzana Fajmonová & Rob Marrs

# Institute of Botany, Academy of Sciences of the Czech Republic 2012

Abstract The increasing importance of the conservation value of managedgrasslands has led to many studies exploring edaphic determinants of grassland biodi-versity. Most studies, however, come either from very large areas, where bio-geographical factors such as dispersal limitation may play a role, or from small, but

Folia GeobotDOI 10.1007/s12224-012-9131-3

L. CachovanováInstitute of High Mountain Biology, University of Žilina, SK-059 56 Tatranská Javorina 7,Slovak Republic

L. CachovanováDepartment of Ecology & Environmental Sciences, Faculty of Science, Palacký University, Olomouc,Czech Republic

M. Hájek : Z. FajmonováDepartment of Botany and Zoology, Masaryk University, Kotlářská 2, CZ-611 37 Brno,Czech Republic

M. Hájeke-mail: [email protected]

Z. Fajmonováe-mail: [email protected]

M. Hájek : Z. FajmonováDepartment of Vegetation Ecology, Institute of Botany, Academy of Sciences of the Czech Republic,Lidická 25/27, CZ-657 20 Brno, Czech Republic

L. Cachovanová : R. MarrsSchool of Environmental Sciences, University of Liverpool, Liverpool L69 3GP, UK

R. Marrse-mail: [email protected]

Present Address:L. Cachovanová (*)Department of Landscape Ecology, Faculty of Natural Sciences, Comenius University, Mlynská dolinaB2, SK-842 15 Bratislava, Slovak Republice-mail: [email protected]

ecologically rather uniform, regions. In addition, few studies further distinguishbetween plant specialists and generalists in the interpretation of the observedpatterns. Here we studied species richness in semi-natural, managed grasslandsin the Strážovské vrchy Mountains in the West Carpathians, Slovakia, wherethere is a matrix of different bedrocks (crystalline, sandstone, claystone, lime-stone) on a steep altitudinal gradient. In 89 vegetation plots we sampled thespecies composition of vascular plants and bryophytes and measured soilchemistry, slope angle, heat index, altitude and soil depth. We further appliedEllenberg indicator values and classified species into community specialists orgeneralists based on the analysis of a large phytosociological database. Usingcluster analysis, we delimited five vegetation types that clearly differed inresponse to soil characteristics. Species richness varied between 19 and 64species per 16 m2. The main compositional gradient correlated with measuredsoil pH and calcium, but species richness was not significantly correlated withthese factors. Soil available phosphorus was not associated with speciescomposition as has been found elsewhere, but it did correlate negatively withspecies richness and the richness of specialists. Overall, species richness waslargely driven by the number of specialists in the plot and particular vegetation typesdiffered conspicuously in their number. We further found significant effects ofiron, potassium and sodium on species richness, species composition and therepresentation of specialists and generalists. Our results provide new insightsinto the determinants of diversity in managed grasslands as well as to thetheoretical species pool concept, explaining species richness variation along apH gradient.

Keywords Community specialists . Environmental gradients . Phytosociology . SoilP. Soil pH .West Carpathians

Plant nomenclature Marhold and Hindák (1998)

Introduction

Species-rich, semi-natural grasslands are an important but threatened habitat through-out Europe (Veen et al. 2009; Reitalu et al. 2010). These meadow and pasture habitatswere first created by humans during the second phase of the Holocene after defores-tation produced conditions where light-demanding plant species could proliferate(Poschlod et al. 2009). These grasslands have been maintained by human activityand many of them are now important natural heritage features throughout most ofEurope because they maintain species-rich communities (Veen et al. 2009; Hájková etal. 2011) containing many endangered species of conservation importance (Šeffer etal. 2002; Jongepierová 2008). Unfortunately, the area of semi-natural grasslandsmanaged using traditional means, i.e., without ploughing, drainage, reseeding andthe use of artificial fertilizers, has decreased significantly throughout Europe over thelast 50 years or so (Šeffer et al. 2002; Piqueray et al. 2011). This reduction hasresulted in the persisting fragments of ancient grasslands that act as refuges for raregrassland species (Šeffer et al. 2002). There is, therefore, an urgent conservation

L. Cachovanová et al.

requirement to understand semi-natural grasslands at the regional scale. There arethree important reasons: first to provide an adequate description of the communitiesthat have survived as a baseline for conservation policy, second to determine thelikely environmental factors that structure these communities so that they can bemaintained, and third to assess their role as refugia of endangered species that mightprovide the basis for expansion into grassland developing on adjacent ex-agriculturalfields (Fagan et al. 2008).

It is well known that grassland species composition is controlled by a set ofinteracting environmental factors (Havlová et al. 2004; Critchley et al. 2007;Kalusová et al. 2009) that includes site-specific conditions such as water regime orsoil physico-chemical properties (Havlová et al. 2004; Marini et al. 2007), climaticconditions (Kalusová et al. 2009) and inter-specific competition as well as the impactof disturbance (e.g., natural events, herbivory, human activities; Grace 1999). Be-cause grasslands have been preserved by traditional management, their diversity alsomirrors historic and present land use (Chýlová and Münzbergová 2008; Başnou et al.2009; Karlík and Poschlod 2009). In addition, changes in nutrient availability havebeen shown to shift vegetation along a successional environmental gradient, with theassociated turnover of specific plant species or even whole communities (Critchley etal. 2007; Rozbrojová and Hájek 2008; Başnou et al. 2009; Karlík and Poschlod2009). Moreover, species-rich grasslands tend to occur on very infertile soils (Marrs1993), and where nutrient concentrations, and particularly phosphorus availability,are increased, it is difficult to re-establish the original grassland communities (Critchley etal. 2002; Başnou et al. 2009). Increasing nutrient supplies (N, P, K) usually bringsabout a decline in species richness (Grime 1979; Janssens et al. 1998; Critchley et al.2002; Marini et al. 2007). Therefore, for the successful conservation and restoration ofsemi-natural grasslands it is important to determine the relative significance of thevarious interacting environmental factors that control plant biodiversity (Corney et al.2006).

Surprisingly there are relatively few studies reporting detailed soil chemistry in aset of spatially and environmentally structured grassland sites (exceptions beingCritchley et al. 2002, 2007; Hájek and Hájková 2004). Most studies have concen-trated on either dry or wet grasslands rather than mesic and semi-dry grasslands (anexception being Ejrnæs and Bruun 2000), presumably because the former harbormore critically-endangered species. Studies of mesic grasslands are either derivedfrom studies over very large areas where biogeographical factors such as dispersallimitation may play a role and where large-scale patterns are detected (Schaffers2002; Kalusová et al. 2009), or from small but ecologically rather uniform regionswhere some environmental gradients are not well developed (Casas and Ninot 2003;Tzialla et al. 2006), or even from a single site (Janišová 2005). Some studies have notmeasured soil characteristics directly but approximated them from plant indicatorvalues (Chytrý et al. 2003; Havlová et al. 2004; Chýlová and Münzbergová 2008) ormeasured only few selected variables (Wagner 2009; Kalusová et al. 2009; Michalcováet al. 2011). Finally, although a series of studies have explored the relationshipsbetween species richness in grasslands and soil chemistry in the context of evolu-tionary species pool hypothesis (Pärtel 2002; Chytrý et al. 2003, 2010), few studieshave distinguished between plant specialists and generalists in the interpretation ofthe observed patterns (Hájek et al. 2007).

Managed Grasslands in a Geologically Heterogeneous Landscape

In this paper, therefore, we explore the relationships between the plant speciesdiversity and local environmental factors including soil chemistry in semi-naturalgrassland communities. We assessed diversity in terms of total species richness,species richness of specialists and variability in species composition. We selected aheterogeneous landscape, i.e., one that had considerable variability with respect toclimate and geology within a rather small region, and which contained a number ofdiverse, managed species-rich mesic, semi-dry and dry grasslands. The region lies inthe southeastern part of the Strážovské vrchy mountains (part of the Inner WestCarpathians, Slovakia) where the underlying geology consisting of crystalline cores(mainly granite) are overlain with limestone and other sedimentary rocks such asclaystones and sandstones. Thus, both acid and calcareous soils occur across thealtitudinal range. This environmental diversity results in very diverse plant commu-nities within a rather small regional area (929 km2), partly as a result of a long historyof low-intensity management as well as its biogeographical position between themontane Carpathian and Pannonian regions (Fajmonová 1995). Despite these advan-tages, there has been no previous detailed study on grassland diversity in this region.Our study, therefore, aimed to do the following: (1) explore the species composition ofsemi-natural grasslands and classify them into distinct community types, (2) analyze therelationships between species composition and environmental gradients, and (3) deter-mine if species diversity (species richness and representation of specialists) is related tolocal environmental factors, and if so, identify the most important ones.

Methods

The Study Area

The study area was located in the Strážovské vrchy Mountains (Fig. 1) along thewestern border of the Inner West Carpathians in Slovakia. The dominant vegetation

Fig. 1 The sites studied in the Strážovské vrchy Mts. (Slovakia, Central Europe) and location of the studyarea in Europe


of the area, and especially in the northern parts, is herb-rich Fagus sylvatica forestdeveloped on limestone bedrock. The grassland communities occur predomi-nantly as semi-natural grassland and pastures with shrub vegetation, sometimeswith the presence of scattered tall shrubs of Juniperus communis. Part of thestudy area has some conservation protection within the Strážovské vrchy Pro-tected Landscape Area (Fig. 1). Meteorogical data indicate a rainfall gradient inmean annual precipitation from 650 mm at the low altitudes in the HornonitrianskaKotlina Basin up to 1,100 mm at the highest altitudes (Strážov hill at 1,213 m). Themean annual temperature varies between 5 °C and 8 °C. The grazing history andhistorical management of meadows in the study area during recent centuries ispoorly documented, but we know from historical aerial photography that thefarmland area has reduced in surface area and become less heterogeneous sincethe 1940s (Fig. 2). There is almost no vegetation composition data available forgrasslands in the study area.

Collection of Vegetation and Environmental Data

Data collection was conducted over four summers between 2006 and 2009 within thelargest grassland complexes in the study area (Fig. 1); these complexes were identi-fied by visual inspection of topographic maps (1 : 25,000). Within each complex,sampling sites were chosen selectively to include only natural and semi-naturalgrassland where there was no evidence (observational or management records) of

Fig. 2 Historical aerial photo-graphs of Čičmany villagewithin the Strážovské vrchyMountain region in a 1949and b 1971. These photographsillustrate the reduction ingrassland use and the homogeni-zation of the landscape. Thepoints indicate position of ourrelevés used in this study. ©Topografický ústav BanskáBystrica, 2010


sowing, recent ploughing and fertilization. Within each sampling site a homogenousplot of 16 m2 was sampled. In total we sampled 89 vegetation plots ranging across analtitudinal gradient from 340 m to 1,199 m a.s.l., from which 52 plots weresampled in the limestone area, 42 plots in the sandstone/claystone area, and 11plots in the crystalline area on granitoids, paragneiss and migmatite. Withineach plot, species composition and abundance, vegetation structure and a rangeof environmental variables were assessed. All vascular plant and bryophytespecies in the plot were identified (nomenclature follows Marhold and Hindák1998) and their abundance scored using the Braun-Blanquet nine-point scale (Westh-off and van der Maarel 1978). Vegetation structure was assessed by estimating thetotal percentage cover of herbaceous vascular plants (including shrub seedlings) andbryophytes. Slope and aspect of each plot was measured and these data along withlatitude were used to estimate heat load according to equation 3 in McCune and Keon(2002).

The type of bedrock was obtained from geological maps (Maheľ 1982) butverified in the field at each plot. Soil depth was measured by inserting a wire (threemeasurements per plot) and classified into two categories: (1) deep soils (soil depth>30 cm) and (2) shallow soils (soil depth ≤30 cm). At four replicate positions withinthe plot the surface vegetation was removed and a sample of the upper soil layer (15×15×20 cm deep) were collected and pooled, air-dried, and sieved to pass through a2 mm mesh. The following variables were then made on each soil: (1) soil pH in a 1:5suspension of dry soil and distilled water; (2) total content of N, C (methods of Zbíral1995–1996); (3) available phosphorus (P) was measured as phosphate concentrationafter extraction in PhosVer3 (for 25 ml; Mehlich III) and (4) exchangeable cations(Fe, K, Na, Ca, Mg) were extracted in 1 M ammonium acetate and analyzed byatomic absorption spectrophotometry.

Vegetation Classification

All vegetation data were stored in the Central Database of PhytocoenologicalRelevés of Slovakia. For analysis the data were then imported into theJUICE 7.0 program (Tichý 2002). The Braun-Blanquet scale data for each specieswere converted into percentages (Tüxen and Ellenberg 1937). Cluster analysisperformed on log-transformed percentage cover data using Ward’s group linkagemethod and the relative Euclidian (Chord) distance was applied (following themethods used in the large-scale study of Rozbrojová et al. 2010) to provide aclassification of the species composition data. The crispness of the classification(Botta-Dukát et al. 2005) was greatest at the 2-cluster level (Groups A and B, Table 1).However, this division was too crude to describe the vegetation variability in ourstudy region, so we subsequently used the OPTIMCLASS1 curve method of Tichý etal. (2010) and Fisher’s Exact Test with a cut level of P<0.01; this produced a steepincrease up to the 5-cluster level and a peak at the 6-cluster level. Because we did notfind any ecological or syntaxonomical interpretation for the last two cluster divisions,we merged them (cluster 1). Thus, we present a final classification at the 5-clusterlevel. For each species and each cluster we further calculated whether the species hada statistically significant affinity (fidelity) to the respective cluster using Fisher’sexact test (P<0.001). For those species whose affinity to a particular cluster was


Table 1 The synoptic frequency table derived from the cluster analysis. The data were split in twodivisions, first into A and B and then into Clusters (1–5); these derived clusters are cross referenced tomuch larger (large-scale) phytosociological studies (Janišová et al. 2007; Rozbrojová et al. 2010). Thesignificant diagnostic species (Fisher’s exact test P<0.001) for both divisions are shown: ** refers to Φvalues >0.4 (standardized to equal group size of 20 % of the whole data set). The classification of speciesinto Specialists (S) and Generalists (G) based on an analysis of large Central-European phytosociologicaldatabase is also shown

First division A A B B B

Cluster Nr. 1 2 3 4 5

Nr. of relevés 35 10 18 14 12

Mesic grasslands (Cluster A) Arrhenatheretalia order (Janišová et al. 2007; Rozbrojová et al. 2010)

Festuca rubra agg. – 91 90 72 71 8

Trifolium repens – 77 80 50 29 33

Anthoxanthum odoratum agg. – 60 80 44 29

Cynosurus cristatus S 43 40 11 7

Filipendula vulgaris S 34 50 14

Carex pallescens S 31 40

Rhytidiadelphus squarrosus – 20 50 7

Cluster 1 (Festuca pratensis-Agrostis capillaris) (Cluster 7; Rozbrojová et al. 2010)

Hypericum maculatum – 63** 40

Ranunculus acris – 83** 50 28 7

Stellaria graminea S 60** 50

Elymus repens G 23**

Rumex acetosa – 80** 80 28 14

Phleum pratense – 49 20 11 14

Agrostis capillaris – 83 90 50 14

Alchemilla species – 66 50 39 14

Veronica chamaedrys – 94 90 56 79 8

Festuca pratensis S 86 50 72 50 25

Campanula patula S 57 80 11 7

Cluster 2 (Viola canina-Agrostis capillaris) (Cluster 7; Rozbrojová et al. 2010)

Potentilla erecta – 9 80**

Danthonia decumbens S 60** 6

Viola canina S 17 70** 6 7

Ranunculus polyanthemos S 11 80** 6 14 17

Semi-dry grasslands (Cluster B) Brometalia erecti (Janišová et al. 2007; Illyés et al. 2007)

Bromus erectus S 6 17 71 75 **

Plantago media – 37 50 89 86 67

Hypericum perforatum G 29 60 78 79 50

Festuca rupicola – 26 50 50 79 83

Linum catharticum – 11 20 78 79 67

Medicago lupulina G 9 30 67 64 25

Salvia pratensis – 3 30 39 43 50

Ononis spinosa – 6 17 43 42


Table 1 (continued)

First division A A B B B

Cluster 3 (Anthyllis vulneraria-Briza media) (Clusters 3 & 4; Rozbrojová et al. 2010)

Anthyllis vulneraria – 3 30 89** 7 50

Salvia verticillata – 6 10 89** 50 25

Dianthus carthusianorum G 9 40 78** 29

Tragopogon orientalis S 43 60 94** 36

Onobrychis viciifolia S 3 33** 7

Thymus pulegioides S 14 70 89** 57

Sanguisorba minor – 11 40 94** 50 67

Asperula cynanchica G 50 14 33

Trifolium montanum S 9 20 50 14 8

Leontodon hispidus S 40 90 100 86 8

Carlina acaulis – 23 40 78 64 8

Viola hirta S 23 20 89 71 75

Polygala comosa S 6 40 56 7 25

Briza media – 40 100 100 86 33

Potentilla heptaphylla – 6 60 94 86 92

Euphorbia cyparissias G 11 40 89 79 92

Cluster 4 (Agrimonia eupatoria-Festuca rupicola)

Bupleurum falcatum – 29 **

Clinopodium vulgare – 3 6 50 ** 17

Vicia tetrasperma G 11 36 **

Fragaria viridis – 9 10 44 86 ** 58

Agrimonia eupatoria S 34 10 44 93 ** 58

Carex tomentosa – 6 33 57 ** 17

Teucrium chamaedrys G 3 10 78 93 83

Cluster 5 (Securigera varia-Bromus erectus)

Dorycnium pentaphyllum agg. – 58 **

Thymus pannonicus – 50 **

Scleropodium purum – 33 **

Securigera varia – 11 20 67 36 92 **

Helianthemum ovatum G 30 6 50 **

Crepis biennis S 20 30 22 21 75 **

Medicago falcata 11 10 78 86 92

Other species with >50 % frequency

Achillea millefolium G 97 100 100 100 92

Lotus corniculatus – 86 90 100 79 92

Dactylis glomerata G 94 80 94 79 8

Plantago lanceolata – 74 80 100 64 58

Pimpinella saxifraga – 51 80 83 86 92

Cruciata glabra S 69 90 94 64 8

Arrhenatherum elatius – 51 60 83 86 67


significant at this P-level, we then calculated the Φ coefficient (Chytrý et al. 2002) ofspecies association with the cluster, standardized to equal group size of 20 % of thewhole data set (Tichý and Chytrý 2006), and highlighted species with Φ>0.40. Theclassification results are shown in a shortened synoptic table (Table 1). To present allimportant species in this table, we further identified the statistically significantdiagnostic species (Fisher’s exact test; P<0.001) for the classification at the levelof two clusters that corresponded to the roughly defined vegetation types of mesicgrasslands (Arrhenatherion elatioris) and semi-dry and dry grasslands (Bromionerecti, syntaxonomical nomenclature according to Janišová et al. 2007).

The final five clusters were interpreted ecologically using both measured environ-mental variables and Ellenberg indicator values. Here, the mean unweighted Ellen-berg indicator values for light (E_L), temperature (E_T), moisture (E_F), reaction(E_R) and soil fertility (E_N) (Ellenberg et al. 1992) for each plot were calculatedusing the JUICE 7.0 Program (Tichý 2002).

Relating Species Community Composition to Environmental Variables

Multivariate ordination was used to assess the effects of environmental, struc-tural and soil characteristics on species composition. The species composition(minus tree and shrub seedlings) of the 89 plots was analyzed and related to23 variables (soil characteristics, slope, heat index, altitude, soil depth, Ellen-berg indicator values, geology, vegetation cover) using Detrended Correspon-dence Analysis (Decorana, DCA) and Canonical Correspondence Analysis(CCA). These analyses described the major compositional gradients and theirrelationships to the measured factors. Analyses were performed using CANOCO4.5 (ter Braak and Šmilauer 2002); here percentage cover data were log-transformed and rare species were down-weighted. The DCA produced a firstaxis gradient length of 2.97 SD. We tested its correlation with measuredenvironmental factors using Spearman correlation coefficients. Correlations withother axes were not tested because of a distorted pattern caused by detrending.CCA was also used to test which of the measured variables accounted for signif-icant amounts of variation in species composition. Here, the forward selectionprocedure in CCA was used to test the significance of each environmentalvariable using a partial Monte Carlo test (9,999 permutations in a reducedmodel); only predictors with a significance of P<0.05 were included in the finalCCA model.

Analysis of Specialist Species

We classified the species into specialists, indifferent and generalist speciesbased on the analysis of a large data set (48,242 plots) of vegetation plots ofall non-forest plant communities in the Czech Republic and Slovakia (Fajmonová Z,Zelený D, Hájek M, unpubl.). Species specialization was calculated using analgorithm written in the R statistical environment (R Development Core Team2008); it was originally developed by Fridley et al. (2007) and modified further byZelený (2009). The algorithm is based on an assumption that habitat specialization ofa given species corresponds to the β-diversity among vegetation plots containing the


target species, assuming that a large enough vegetation dataset is available toencompass a very wide range of environmental conditions. According to thismetric, the species occurring in plots of similar species composition can beinterpreted as being a habitat specialist, because the similarity in speciescomposition among plots indicates that the species grows only on narrowrange of habitat types. However, species occurring in plots that differ consid-erably in species composition (and hence by habitat conditions) can beinterpreted as habitat generalists. β-diversity among plots was quantified byWhittaker’s multiplicative measure (Whittaker 1960). Species in the dataset differin their frequency of occurrence, which may lead to the situation when frequentspecies will tend to be habitat generalists simply because they have higher probabilityof occurring in different habitats. To overcome this problem, five plots containingthe target species were randomly chosen for calculation of beta diversity; thisrandom selection was repeated 100 times and the average of calculated betadiversity values was used as the measure of species specialization (θ). Thismeasure was calculated for all species with five and more occurrences. Thespecies were then ranked on the basis of their θ value; 33 % of species withthe lowest θ (low beta diversity) were classified as specialists, 33 % with thehighest θ (high beta diversity) were classified as generalists and the interme-diate 33 % were classified as indifferent species. The θ value and thespecialization status was assigned to most species in our dataset except fori) species that had fewer than five occurrences in the non-forest vegetation dataset andii) species aggregates that may collectively cover several different ecological niches(e.g., Alchemilla vulgaris agg., apomictic Rubus and Taraxacum species).

Here, we calculated the number of specialists, generalists, the specialist/generalistratio and the proportion of specialists in each vegetation plot. In this way, the specieshabitat specialization could be transferred to an index of community specialization(Devictor et al. 2010).

Assessing Differences Between Plant Communities

All univariate analyses were performed using SPSS v14.0 (SPSS 2005). One-way analysis of variance followed by Tukey’s post-hoc multiple comparison test wasused to check for differences in the number of all species, the number of specialistsand the environmental variables among the clusters obtained from the cluster analy-sis. We also tested, separately for each cluster, if the proportions of specialistsin the clusters differ from the total proportion of specialists across all assessedspecies occurring in our data set (20.3 %), using a one-sample t-test. The totalproportion of specialists represents their hypothetical proportion in the commu-nities, provided that specialists were distributed randomly in the vegetationstudied. This analysis tests whether the number of specialists in the vegetationplots belonging to a cluster is higher or lower than would be expected byrandom chance. Spearman correlation coefficients were then used to assess therelationships between the number of all species, the number of specialists andmeasured environmental factors. Because this analysis can reveal only linearrelationships, we further checked for insignificant results if the relationship isunimodal, using quadratic regressions.


Results

Vegetation Classification and Relationship with Environmental Variables

The cluster analysis of the 89 plots containing 324 species led to five interpretableclusters (Table 1); these clusters were also cross-referenced to two large-scale phy-tocociological studies (Illyés et al. 2007; Rozbrojová et al. 2010). The first divisionsplit the data into two clusters; one side (A) represented mesic grasslands thatcorresponded to the Arrhenatheretalia order (Janišová et al. 2007; Rozbrojová et al.2010), whereas the other side (B) represented drier grasslands on more alkaline soilsbelonging to the Brometalia erecti order (Janišová et al. 2007; Illyés et al. 2007).Within Cluster A, two subsidiary clusters were derived: (1) a Festuca pratensis-Agrostis capillaris community representing mesic, productive grasslands with abun-dant grasses (Festuca rubra, F. pratensis, Agrostis capillaris, Arrhenatherum elatius,Phleum pratense, Elymus repens), and (2) a Viola canina-Agrostis capillaris com-munity, a less-productive grassland with a substantial component short-stature spe-cies, e.g., Danthonia decumbens, Viola canina, and Potentilla erecta. Cluster B splitinto three clusters: (3) an Anthyllis vulneraria-Briza media species-rich communitywith a high representation of broad-leaved species and is a transitional type betweenmesic and dry grasslands; (4) and (5) are more clearly defined Bromion erecti grass-lands, named Agrimonia eupatoria-Festuca rupicola (4) and Securigera varia-Bromus erectus (5) in our study and corresponding largely to Scabiosoochroleucae-Brachypodietum pinnati (Cluster E, Illyés et al. 2007) and Onobrychidoviciifoliae-Brometum erecti (not clearly delimited in Illyés et al. 2007) associations,with Bromus erectus and Festuca rupicola as dominant species. Cluster 3, whichappeared to be very important in terms of biodiversity conservation here, showscertain similarities to the Anthyllidi-Trifolietum montani association, described fromthe Polish Carpathians (Matuszkiewicz 2007), but this association has not beenrecognized in Slovakia yet (Janišová et al. 2007).

The relationship of these five clusters with respect to all significant environmentalvariables (Fig. 3) show that there is a great degree of overlap in the range ofvariables between the groups. In spite of this several important patterns werederived. First, the unweighted mean E_F and E_N suggest a moisture andfertility gradient from the more mesic Cluster 1 through to the drier Cluster5; temperature was lower in the most mesic Cluster 1 community compared tothe others. Second, there was also a soil pH (median range 5.6–7.6) andexchangeable Ca gradient (median range 2,722–5,587 mg/kg), with lower val-ues in the Arrhenatheretalia group (Clusters 1 and 2) and greater in the Brometaliaerecti group (Clusters 3, 4 and 5); this increase in alkalinity was reflected in the soilexchangeable K and Fe concentrations and total N and C concentrations: K increasedalong the pH gradient being greatest in the Clusters 3 and 4 and Fe showed a negativecorrelation with soil pH.

Community Analysis

The first two DCA axes explained 11.6 % and 4.3 % of the variation in the speciesdataset. The species distributions (Fig. 4a,b) show a good spread along both axes


although the most frequent species are clustered in the upper right quadrant. Thedistribution of the most frequent species and the quadrats suggests a productivitygradient on both axes (Fig 4b,c); axis 1 produced a gradation from the productiveAgrostis grassland (Clusters 1 & 2) at the negative end through productive Bromusand herb-rich Briza grasslands (Clusters 3 & 4) through to the dry Bromus grasslands(Cluster 5) at the positive end; on axis 2 the low-productive Agrostis grasslands andthe herb-rich Briza grassland (Clusters 2 & 3) were positioned below the other three

Fig. 3 Box plots describing the environmental variables (E_F – Ellenberg moisture; E_N – Ellenbergnutrients, E_T – Ellenberg temperature) found in the five vegetation clusters (Table 1) found within the theStrážovské vrchy Mountain region; for each variable the minimum, median, maximum and lower and upperquartiles are presented. Significant differences among vegetation types determined using the Tukey post-hoc test) are denoted using lower case letters

�Fig. 4 Plots of first two axes derived from the Detrended Correspondence Analysis (DCA) of thegrassland communities in the Strážovské vrchy Mountain region. a Species plot with all species and the25 most frequent ones, b Subset of species plot identifying the 25 most frequent species, c Quadrat plotidentified by the five cluster derived from the classification analysis (Table 3), d passive overlay of thesignificant environmental variables. Codes: b Species: Achimil – Achillea millefolium agg., Agrocap –Agrostis capillaris, Arrhela – Arrhenatherum elatius, Brizmed – Briza media, Bromere – Bromus erectus,Centjacv – Centaurea jacea, Cruggla – Cruciata glabra, Dactglo – Dactylis glomerata, Euphcyp –Euphorbia cyparissias, Festpra – Festuca pratensis, Festrub – Festuca rubra, Festrup – Festuca rupicola,Galimol – Galium mollugo agg., Leonhis – Leontodon hispidus, Lotucor – Lotus corniculatus, Medifal –Medicago falcate, Pimpsax – Pimpinella saxifraga agg., Planlan – Plantago lanceolata, Planmed –Plantago media, Poa pra – Poa pratensis, Potehep – Potentilla heptaphylla, Teuccha – Teucriumchamaedrys, Thympul – Thymus pulegioides, Trifrep – Trifolium repens, Verocha – Veronica chamaedrys;d Environment – see text


clusters. The correlation between the axis scores and environmental variables (Table2, Fig. 4d) showed that most were aligned with the main productivity gradient on axis 1,with the most productive Agrostis grasslands (Cluster 1) being highly correlated withaltitude, vegetation and total cover, and Ellenberg N and F values and crystallinerocks, and the dry Bromus grasslands (Cluster 5) being correlated with soil pH,extractable Ca, K, Mg, total N and C, deep soils on limestone and Ellenberg R, Land T.


Table 2 The correlation between the first axis of a DCA of species composition data from the Strážovskévrchy Mountains study area in Slovakia and measured environmental variables and vegetation variables,vegetation cover and unweighted means of Ellenberg indicator values (E_L – Light, E_T – Temperature,E_F – Moisture, E_R – Reaction (pH), E_N – Soil fertility). Spearman’s correlation coefficients arepresented and significant relationships were detected between 13 measured variables and the first DCAaxis (P≤0.05; P≤0.01 are in bold). Crystalline refers to the crystalline bedrock, i.e., granite in most cases,and sedimentary refers to sandstone and claystone

Environmental variables DCA axis 1

pH 0.819

Total C 0.386

Extractable P

Total N 0.282

Exchangeable Fe −0.52

Exchangeable K 0.454

Exchangeable Ca 0.544

Exchangeable Mg 0.464

Heat index 0.464

Altitude −0.433Soil depth 0.529

Limestone 0.443

Crystalline −0.316

Vegetation variables

Vegetation cover −0.355E_L 0.584

E_T 0.434

E_F −0.906E_R 0.851

E_N −0.771

Table 3 The results of the stepwise selection of the environmental variables using forward selectionprocedure in CCA

Step Environmental variables F-ratio P-value % from all variables % from total inertia

1 Soil pH 7.439 0.0001 25.8 8.1

2 Elevation 2.858 0.0001 9.7 3.0

3 Soil depth (>30 cm) 2.215 0.0001 7.4 2.3

4 Soil exchangeable Na 2.067 0.0002 6.8 2.2

5 Sedimentary bedrock 1.825 0.0001 5.9 1.9

6 Soil exchangeable Fe 1.742 0.0003 5.7 1.8

7 Crystalline bedrock 1.612 0.0009 5.2 1.7

8 Soil total organic C 1.551 0.0014 4.9 1.5

9 Soil exchangeable Mg 1.644 0.0006 5.2 1.7


The CCA analysis produced a slightly different picture; the first two axeswere correlated significantly (P≤0.001) with nine environmental variables (Table 3)and explained 12.8 % of the total inertia (Fig. 5). The species distribution was fairlysimilar to the DCA plot, however, the relative importance of the geological substrateas explanatory variables was greater than in the DCA. In this analysis soil pH gradientaccounted for most of the variation on axis 1, with lower pH values in the crystalline,granitic rocks. This suggests that the major structuring feature in this vegetation is thesolid geology gradient (crystalline (granite) to limestone), which is correlated withsoil pH. The other significant explanatory environmental variables were altitude, soildepth, soil Na, Fe, Mg, organic C and geological bedrock (Table 3). When all these

Fig. 5 Plots derived from the Canonical Correspondence Analysis (CCA). a Species plot with all species andthe 25 most frequent ones, b The significant environmental variables (P≤0.001), c Subset of species plotidentifying the 24 most frequent species. Species codes: Agrocap – Agrostis capillaris, Achimil – Achilleamillefolium agg., Anthodo – Anthoxantum odoratum, Arrhela – Arrhenatherum elatius, Brizmed – Brizamedia, Centjac – Centaurea jacea, Crucgla – Cruciata glabra, Dactglo – Dactylis glomerata, Euphcyp –Euphorbia cyparissias, Festpra – Festuca pratensis, Festrub – Festuca rubra, Festrup – Festuca rupicola,Galimol – Galium mollugo agg., Leonhis – Leontodon hispidus, Lotucor – Lotus corniculatus, Medifal –Medicago falcata., Pimpsax – Pimpinella saxifraga agg., Planlan – Plantago lanceolata, Planmed –Plantago media, Poa pra – Poa pratensis, Teuccha – Teucrium chamaedrys, Thympul – Thymus pule-gioides, Trifrep – Trifolium repens, Verocha – Veronica chamaedrys; Environment codes, see text


variables were used in the final model, the first axis represented increasing pH (andtotal soil C and exchangeable Mg), and decreasing soil exchangeable Fe concentra-tion, and the second axis produced a gradient from deeper soils with high soilexchangeable Mg concentrations (positive scores) on crystalline bedrock through toshallower soils on sedimentary rocks at the higher altitudes with high soil exchange-able Na concentrations (negative scores, Fig. 5).

Species Richness

Generally, overall species richness was driven largely by the number of specialistsin the plot. The species richness per plot varied between 19 and 64 (mean 44species) and the number of specialists varied between 3 and 20 (mean 11 species).Unlike the community composition, species richness was not significantly corre-lated either with soil pH or exchanegable Ca concentration (linear or quadratic).However, a significant correlation was detected between the proportion of general-ists and both soil pH and exchangeable Ca concentration (Table 4). Species richness

Table 4 Relationship between species richness (number of herbaceous species in plot), number ofspecialist species, specialists:generalists ratio and both the proportion of specialists and generalists in eachplot and environmental variables. Spearman’s correlations coefficients are presented; only siginficiantcoefficients (P≤0.05 (P≤0.01 are in bold)) are presented. n.s. – not significant

Variable Speciesrichness

Number ofspecialist species

Specialist:Generalist ratio

Specialists(%)

Generalists(%)

Species richness – 0.756 0.361 0.403 0.395

Number of specialists 0.756 – 0.829 0.888 n.s.

Specialist:Generalist 0.361 0.829 – 0.93 −0.616Specialists (%) 0.403 0.888 0.93 – −0.414Generalists (%) 0.395 n.s. −0.616 −0.414 –

pH n.s. −0.218 −0.395 −0.389 0.398

Soil total C n.s. n.s. −0.288 −0.253 0.397

Soil extractable P −0.217 −0.263 −0.249 −0.227 n.s.

Soil total N n.s. n.s. n.s. n.s. 0.361

Soil exchangeable Fe n.s. 0.298 0.389 0.371 −0.241Soil exchangeable Na n.s. n.s. −0.263 −0.244 0.246

Soil exchangeable K n.s. n.s. −0.286 −0.263 0.373

Soil exchangeable Ca n.s. n.s. −0.371 −0.319 0.427

Soil exchangeable Mg n.s. n.s. −0.275 n.s. 0.307

Ellenberg’s temperature n.s. n.s. −0.29 −0.3 n.s.

Ellenberg’s moisture n.s. n.s. 0.294 0.303 −0.281Ellenberg’s reaction n.s. −0.224 −0.414 −0.424 0.404

Elevation n.s. 0.25 0.216 0.217 n.s.

Limestone n.s. n.s. −0.279 −0.242 0.354

Sedimentary n.s. 0.29 0.291 0.283 n.s.

Vegetation cover 0.256 0.222 n.s. n.s. n.s.


overall, and especially the number of specialist species, were correlated negatively withsoil extractable P concentration, although a reduction in species richness and thenumber of specialists was, however, recorded only in plots with soil P contentabove 230 mg/kg. The number of specialists was also correlated positively withsoil exchangeable Fe, which tended to be greatest on sedimentary bedrock(sandstone).

The greatest number of species was found in Cluster 2, 3 and 4, and for specialistspecies in Clusters 2 and 3 (Fig. 6). The lowest species richness was found in Cluster 1,the most productive, mesic community, and Cluster 5, the driest calcareous grasslandsfound at low altitudes; Cluster 5 also had the lowest number of specialist species. Whenthe mean proportions of specialists in these clusters were compared with the hypothet-ical value expected from the random distribution of specialists in the studied vegetation(20.3 %), the first three clusters (mesic and transitional grasslands) had a greaterproportion of specialists than would be expected by random (Proportions027.1 %,

Fig. 6 Box plots describing aspecies richness, and b numberof specialist species found in thefive vegetation clusters (Table 1)found within the the Strážovskévrchy Mountain region; foreach variable the minimum,median, maximum and lower andupper quartiles are presented.Significant differences amongvegetation types determinedusing the Tukey post-hoc test aredenoted using lower case letters


32.3 % and 27 %, t-values05.17, 7.17, 5.94, respectively for Clusters 1, 2 and 3, allP<0.001). Cluster 4 did not differ significantly from the expected value (Proportion022.4 %, t-value02.16) whereas the driest low-altitude calcareous grasslands ofCluster 5 had a significantly lower representation of habitat specialists (Proportion017.3 %, t-value02.79, P<0.02).

Discussion

Vegetation Classification

Five plant communities were described from this diverse region. Two of theclusters described here are dry grasslands of the Bromion alliance (Clusters 4 and5); these correspond well to published plant associations (Janišová et al. 2007).However, Clusters 1, 2 and 3 of rather mesic and semi-dry grasslands are lessclear-cut. They correspond approximately to Clusters 3, 4, 7 and 10 in the study ofRozbrojová et al. (2010), but these clusters contained a very heterogeneous mixtureof grassland types whose classification was made only within the higher-rank syn-taxa. Usually, these grasslands are classified within the Anthoxantho odorati-Agrostietum tenuis association, but this conception needs a revision (Janišová et al.2007; Rozbrojová et al. 2010). Cluster 3, which appeared to be a very diversecommunity, has not been recognized within the Slovakian phytosociological systemyet (Janišová et al. 2007), but it clearly warrants further study because of its highconservation value.

Vegetation Gradients

The major compositional gradient appeared governed by a dominant gradient of soilpH that was associated with geological substrate and a range of associated soilchemical properties (low concentrations of exchangeable Fe and P on calcareoussoils). This result mirrors the results of many other studies on temperate vegetationfocusing not only on mesic and semi-dry grasslands (Critchley et al. 2002; Wagner2009) but also on fens (Hájek et al. 2002, 2007), wet grasslands (Hájek and Hájková2004), dry grasslands (Ejrnæs and Bruun 2000; Balkovič et al. 2010) and tundra(Virtanen et al. 2006). The generality of these relationships might be explained by thecalcifuge-calcicole response of plants that is driven either by P or Fe deficiency onstrongly calcareous soils, or by aluminium toxicity on acidic soils (Tyler 2003).While P deficiency in calcareous soils has been demonstrated especially in wetlands(Rozbrojová and Hájek 2008), tolerance to Fe deficiency is considered as a determi-nant of calcicole behaviour of plants in drier habitats (Grime 1965; Zohlen and Tyler2004). On acidic soils, however, concentrations of Fe may be directly importantbecause Fe is toxic to calcicole species (Rozbrojová and Hájek 2008), especiallyunder reducing conditions (Martin 1968), or indirectly because it can immobilizephosphates (Kooijman and Hedenäs 2009). Our results support that Fe has an impor-tant effect because it correlated strongly with the first DCA axis. Such a strongrelationship between Fe concentration and vegetation diversity has only been describedin wetlands (Rozbrojová and Hájek 2008; Vermonden et al. 2010).


The soil chemistry variation described in our study is to some extent predicted bythe underlying geological bedrock, with clear differences observed between crystal-line and limestone substrate (Fig. 3). The chemistry of sedimentary bedrock inparticular was variable, being a mixture of non-calcareous sandstones cemented byFe and strongly calcareous rocks such as limestone. Some sandstones in the WestCarpathians are exceptionally rich in Fe and this influences groundwater quality(Hájek et al. 2002) as well as the soil chemistry as demonstrated here.

From the major soil-available nutrients, only the soil K concentration correlatedstrongly with the major compositional gradient. Of particular note was the high Kconcentration associated with the Agrimonia eupatoria-Festuca rupicola community(Cluster 4), which was higher than in other vegetation types. Such a conspicuouslyhigh K-concentration in this dry-grassland vegetation type might be caused by theunderlying geology (claystones rich in minerals) and may explain the more frequentoccurrence of species typical of productive mesic grasslands. Soil organic N and soilP displayed very weak relationships with the main compositional gradients and nostatistically significant differences among the vegetation types, contrary to some otherstudies from grasslands (Hájek and Hájková 2004; Marini et al. 2007; Hejcman et al.2010).

Species Richness

The fact that overall species richness was driven largely by the number of specialistsin the plot corresponds to the result from Swedish grasslands (Öster et al. 2007), but itcontrasts the results from mires (Hájek et al. 2007). This discrepancy might be becausewhile grasslands often exist within a landscape matrix, mires are island habitats whoselocal species richnessmay be influenced by the number of generalist species entering thesite from the surrounding area (Hettenbergerová and Hájek 2011).

From all directly measured environmental factors, only soil P displayed a statis-tically significant relationship with total species richness. The reduction in speciesrichness in grasslands on P-rich soils has been recorded in many studies throughoutEurope (Janssens et al. 1998; Hejcman et al. 2010), but in many studies increased Pavailability also alters community species composition (Marini et al. 2007; Hejcmanet al. 2010). In our study, the species richness was not associated with the differencesin soil P among vegetation types. The plots with high soil P content and low speciesrichness belonged to different vegetation types, i.e., Clusters 1, 4 and 5 (i.e., bothmesic and dry grasslands). Moreover, we also showed that the low species richnesson P-rich sites was largely caused by the low representation of grassland habitatspecialists. In a similar vein, other studies that have reported a decrease in speciesrichness in P-rich found a reduction in S-strategy plant (Marini et al. 2007) or lowerdiversity indices (Janssens et al. 1998) or numbers of Red-list species (Wassen et al.2005). However, our findings are novel because they demonstrate the loss of grass-land specialists in P-rich sites even thought vegetation type is not changed. The fact thatspecies richness decline on P-rich stands is governed by its impact on habitat specialistseven when total species composition is not fundamentally changed is crucial from abiodiversity conservation viewpoint.

The positive relationship between the representation of habitat specialists and soilFe has no analogy in the literature. This surprising result may mirror either P-


immobilization by Fe, which is more often reported from wetlands than from grass-lands (Kooijman and Hedenäs 2009), or by Fe toxicity supressing generalist plantsthat are often C-strategy, not S-strategy plants (Grime 1979).

We found no significant relationship between total species richness and the pH/Cagradient. The relationship between pH and the species richness is usually observed intemperate vegetation, and is explained by historical factors such as evolutionary andrefugial history (Pärtel 2002; Ewald 2003; Chytrý et al. 2010). In grasslands, thisrelationship is not always manifested. No relationship between pH and grasslandspecies richness was found by Janssens et al. (1998) and for a subset of mesicgrasslands by Chytrý et al. (2003). The latter study, which used Ellenberg indicatorvalues and a large phytosociological database, concluded that mesic grasslands areman-made and of modern origin, and historical factors, therefore, did not play a role.Higher species richness of neutral or basic soils, however, was found in mesicgrasslands by Critchley et al. (2002). Spiegelberger et al. (2010) found an increasein species richness on limed grasslands, but this increase was caused by generalists.This result supports our observations: when the species richness was partitioned intospecialists and generalists, soil pH and exchangeable Ca concentration correlatedpositively with the species richness of generalists. It suggests that although greatercommonness of alkaline soils in evolutionary centers supported adaptations to alka-line soils (Pärtel 2002; Chytrý et al. 2010), many species adapted to alkaline soilshave a rather broad niche with respect to other environmental factors and occur invery different vegetation types developed on alkaline soils. Some less competitive yetsuccessful species, however, may have become adapted to less common habitats, forexample low-productive acid grasslands. Moreover, generalist species are largelythose that can inhabit different vegetation formations, for example forest edges aswell as grasslands or wetlands. Increasing representation of generalist species withincreasing pH in young, man-made habitats, may, therefore, be caused simply by theincreasing representation of calcicole species in natural habitats such as forests orancient dry grasslands that have acted as a source of species inhabiting newlyappearing man-made grasslands (Chytrý et al. 2003).

Acknowledgements We thank Petr Hekera for assistance with sample processing and technical assistance,David Zelený for calculating specialization, Zbyněk Hradílek, Tomáš Berka and Martin Dančák for their helpwith critical species identification, Jana Smatanová for providing literature, Jarko Solar for map production andMrs S. Mather for producing the final diagrams. This research was supported by the doctoral grant projectGD526/09/H025 and long-term development project of the Czech Academy of Sciences no. RVO 67985939.

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Received: 17 June 2011 /Revised: 5 December 2011 /Accepted: 27 February 2012


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