INTRODUCTION

Ultramafic (serpentine) soils typically host a dis-tinctive flora and vegetation, largely affected bythe edaphic and physical characters of these soils(Brooks 1987; Baker et al. 1992; Roberts & Proctor1992). In Tuscany, central Italy, serpentine vege-tation has been investigated over a long period andwith different approaches (e.g. Messeri 1936; PichiSermolli 1948; Chiarucci et al. 1998b). The appar-ent lack of vegetation dynamics on these soils hasbeen ascribed to the negative effects of soil metal.Recent studies provide evidence of a highercontent of exchangeable metals in the soil under-lying the later successional stages, suggesting thatfactors other than soil metals are limiting.

Chiarucci et al. (1998a, 1998b) hypothesizedthat the physical conditions present at a site (e.g.slope, solar heating) and the nutrient content ofthe soil have a greater influence on the species com-

position than the negative affect of soil metalcontent. These studies were carried out with explo-rative aims and the sampling sites were placed inareas with physiognomically homogeneous plantcover. Although obtained with subjective sam-pling, the relationships were found at differentspatial scales: 4 m2 plot as grain and a single siteas extent by Chiarucci et al. (1998a); 100 m2 plotas grain and the whole Tuscany as extent byChiarucci et al. (1998b). Here we use an objectivesampling method to test the hypothesized relativestrength of the relationship between vegetationcomposition and environmental factors and a finerspatial scale to reduce the effects of within-plotheterogeneity.

Large parts of the serpentine outcrops of Tuscanyhave been planted with pines. These plantationshave a negative affect on serpentine vegetation, by modifying the plant community structure andspecies composition, in particular by reducing theabundance of endemic species in favor of commonand/or more competitive species (Chiarucci & DeDominicis 1995; Chiarucci et al. 1996, 1998a). Thespread of pines introduced in the last few decadesin the Upper Tiber Valley threaten grasslands

Ecological Research (2001) 16, 627–639

A test of vegetation–environment relationship in serpentinesoils of Tuscany, Italy

Alessandro Chiarucci,* Duccio Rocchini, Claudio Leonzio andVincenzo De Dominicis

Dipartimento di Scienze Ambientali, Università di SienaVia P.A. Mattioli 4, 53100 Siena, Italy

The present study evaluates the relative importance of environmental factors in affecting the speciescomposition and abundance of the plant communities on ultramafic soils in Tuscany, Italy. We usedrigorous sampling techniques to test hypotheses generated from exploratory studies performed previously. Vegetation–environmental relationships were analyzed using 50 plots, each 1 m2, ran-domly located throughout a 22-ha area in the Upper Tiber Valley. We confirm that the exchange-able fraction of nickel in the soil is almost never high enough to affect the vegetation. However,physical factors (e.g. substrate setting and elevation) are important in controlling the distributionof plant species. Tree cover (almost exclusively due to the introduced plantation pines) also had asignificant affect on the vegetation composition and on soil features such as the C/N ratio. Otherimportant factors significantly related to the gradients in vegetation composition (e.g. rockiness andtotal soil nitrogen) are interpreted as factors related to the vegetation composition through a posi-tive feedback mechanism.

Key words: endemic plants; monitoring; positive feedback; restoration; ultramafic soils.

*Author to whom correspondence should beaddressed. Email:chiarucci@unisi.it

Received 28 February 2001.Accepted 27 June 2001.

628 A. Chiarucci et al.

dominated by Stipa tirsa Steven (Chiarucci et al.1996), a rare species present in the whole Italianpeninsula only at this site (Pichi Sermolli 1948;Moraldo 1986). At this site, an area was selected foran applied project with the aim of restoring the siteto the habitat existing before the pine plantation.A monitoring plan was set up with the establish-ment and sampling of 50 plots before the pineremoval. This data set offered the opportunity to test the vegetation–environmental relationshiphypothesized by Chiarucci et al. (1998a, 1998b).We restricted our investigation to the herbaceousand shrub layers as the distribution of most of thetree species has been directly influenced by humanactivity, either in a positive or a negative way.

METHODS

Study site

The study site is located in the Monti Rognosi ofAlbiano (43°33¢17≤ N, 11°57¢30≤ E), in the south-ern group of ultramafic outcrops of Upper TiberValley, Tuscany (Pichi Sermolli 1948). It is approx-imately 22 ha in extent and ranges in elevationfrom 420 to 575 m a.s.l., with varying slope exposures. Mean annual temperature is 13.1°C.The average temperature of the coldest month(January) is 4.4°C and the average temperature of the warmest month (July) is 22.4°C. Annualrainfall averages 897 mm, November being thewettest month with an average of 114 mm andJuly the driest month with an average of 43 mm(data from the climatic station of Sansepolcro, Bigi& Rustici 1984).

The typical serpentine vegetation consists ofgarigues (chamaephytic and annual plants onshallow or rocky soils) and grasslands. However,the present vegetation is mostly dominated by thesparsely distributed and introduced pines (Pinuspinaster Aiton, P. pinea L., P. nigra Arnold), withtypical serpentine vegetation restricted to openpatches and slopes; pines were planted in differentperiods, during the second half of the 20th centurywith varying density, and then spread by dissemi-nation. Alnus cordata (Loisel.) Desf., another intro-duced species, was also planted in some areas. Inaddition, some native deciduous trees are present:the most common naturally occurring species

being Fraxinus ornus L.; Quercus cerris L., forms aclump in a localized area, Alnus glutinosa (L.)Gaertner is present along a small stream withperennial water flow and other species (Sorbus aria(L.) Crantz, Frangula alnus Miller, Ostrya carpinifo-lia Scop., Quercus pubescens Willd.) are sparselypresent.

Data collection

Fifty sample plots were located by randomly gen-erating spatial coordinates. To reduce within-plotheterogeneity and obtain a better relationshipbetween the soil data and vegetation data, a plotsize of 1 m2 was selected. The plots were locatedusing a GPS (Global Positioning System) receiverand were marked for future monitoring. Environ-mental variables were collected when the plot waslocated: altitude, aspect, slope, type of substrate,tree layer cover was visually estimated in a 5-mradius around the plot. Total vegetation cover (%),rockiness (%) and bare soil (%) were estimated inthe same manner as individual species cover andwere recorded during vegetation measurement inthe plot. In June 2000, when most of the specieshad reached peak biomass and were flowering orfruiting, all vascular plants growing within eachplot were recorded and their cover estimated usingthe point-quadrat method (see Moore & Chapman1986), with a density of 100 pins m–2. Plantspecies present within a plot but not touched byany pin were registered with an arbitrary cover of0.1%; a pin hit was classified as rocky if it touchedpart of an outcropping rock or a stone larger than10 cm. The index of normalized irradiance thatwas used (Bartorelli 1967) summarizes the inter-active effects of latitude, slope and aspect, givinga measure of the radiation (in hours per year ofoverhead sunlight) the site actually receives in 1year. Codes of environmental variables and theirrange of variability are given in Table 1.

Plant nomenclature follows Chiarucci et al.(1995) for serpentinophytes, Moraldo (1986) forthe genus Stipa and Pignatti (1982) for otherspecies.

Soil sampling and analysis

A composite soil sample was taken in each plotafter removing the surface organic layer. Soil

Vegetation–environm

ent relationships629

Table 1 Environmental variables measured, with their abbreviated names, unit of measurement, range of measures and additional notes

Symbol/ Unit ofVariable Abbreviation measurement Range Notes

Elevation Elev Meters 420–575Aspect Asp Degree from N 15–290Slope Slop Degree 5–50Tree cover Tree % 0–100 Cover of the tree layer within 5 m from the center of the plotRockiness Rock % 0–89 Percentage of the plot with outcropping rockBare soil Soil % 0–88 Percentage of the plot with bare soilExchangeable soil chromium Cr mg g–1 0.0–0.1 Lower limit of detectability. Not included in analysesExchangeable soil zinc Zn mg g–1 0.0–0.2 Lower limit of detectability. Not included in analysesExchangeable soil nickel Ni mg g–1 0.3–6.2 Log transformed for analysisExchangeable soil copper Cu mg g–1 0.0–0.1 Lower limit of detectability. Not included in analysesExchangeable soil cobalt Co mg g–1 0.0–0.2 Lower limit of detectability. Not included in analysesExchangeable soil iron Fe mg g–1 0.0–0.6 Lower limit of detectability. Not included in analysesExchangeable soil manganese Mn mg g–1 0.3–11.4 Log transformed for analysisExchangeable soil magnesium Mg mg g–1 136.4–392.9 Log transformed for analysisExchangeable soil potassium K mg g–1 3.3–21.3 Log transformed for analysisExchangeable soil calcium Ca mg g–1 81.5–499.2 Log transformed for analysisExchangeable soil sodium Na mg g–1 1.8–8.2 Log transformed for analysisMagnesium to calcium ratio Mg/Ca 0.5–4.1Total organic carbon C org % 0.3–14.0Total nitrogen N tot % 0.02–0.88Carbon to nitrogen ratio C/N 9.9–34.0Soil pH pH 5.58–7.11Solar irradiance Irrad h/year 1335–2619 Hours per year of overhead sunlightSubstrate setting Debris Dummy variable Two geological settings (outcropping rocks vs slope debris)

630 A. Chiarucci et al.

samples were air dried and passed through a 125-mm sieve. A 1:2.5 soil–water suspension was usedto measure pH; organic carbon and total nitrogencontent were measured by CNS NA 1500 CarloErba instrument (Carlo Erba Instruments, Milano,Italy). Exchangeable cations were extracted byadding 30 ml of 1 m CH3COONH4 to 3 g of soilin polythene containers and shaking them over-night. The solutions were filtered and analyzed byPlasma Emission Spectrometry (ICP, mod. PerkinElmer, Plasma 400 (Perkin Elmer, Shelton, CT,USA)). The exchangeable fraction, as opposed tothe total concentration, was determined because itgives a measure of the concentration available toplants. The Mg/Ca ratio of the exchangeable frac-tion and the C/N ratio were also calculated.

Data analysis

Plots were grouped into floristically similar vegetation types using cluster analysis using SYN-TAX 5.0 (Podani 1993): average linkageagglomeration criterion was applied to a matrixconstructed using the Jaccard similarity index(Podani 1994).

We used both an unconstrained ordination (cor-respondence analysis: CA) and a constrained ordi-nation (canonical correspondence analysis: CCA) tofirst see how much of the variation in the speciescomposition could be explained by ordination(unconstrained) and then to relate species data tothe measured environmental variables (constrainedordination). In unconstrained ordination the ordi-nation axes are related to floristic data and they canthen be related to the environmental data. In con-strained ordination, the axes are constrained to be linear combinations of the environmental vari-ables (ter Braak 1986, 1987). Correspondenceanalysis and CCA (ter Braak 1986) were performedusing canoco 4.02 (ter Braak 1988; ter Braak &Smilauer 1998). We used biplot scaling focused oninterspecies distances, square-root transformedcover data of species and downweighted rarespecies. A Monte Carlo permutation test wasapplied to test the significance of the eigenvaluecorresponding to the CA and CCA canonical axes(ter Braak 1988). When it is used in an exploratoryway, CCA leads to an ordination diagram ofsamples, species and environmental variables,which optimally displays how community compo-

sition varies with the environment. When used ina confirmatory way, as is the case here, CCA leadsto statistical tests of the effects of particular envi-ronmental variables on community compositiontaking into account the effect of other variables (ter Braak 1986, 1987; Palmer 1993; ter Braak &Smilauer 1998). In order to reduce the negativeeffects of a large number of constraining variableson the direct ordination and to simplify the inter-pretation of CCA results, the number of variableswas reduced after checking their marginal and con-ditional effects. A dummy variable was used forbedrock type, indicating in a categorical way thetwo geological substrates found in the area (‘out-cropping ultramafic rocks’ and ‘slope ultramaficdebris’).

RESULTS

Species richness and vegetation types

A total of 82 vascular plant species were found inthe 50 plots of 1 m2 each. Species richness per plotvaried between 3 and 16, averaging 9.6 ± 0.4 (SE).Almost all the species had low frequency values:28 species (34.1% of the total) were found in onlyone plot and 59 species (72.0%) were found witha frequency lower or equal to five plots. Carexhumilis Leyser was the most frequent species (27plots), followed by Bromus erectus Hudson, Festucarobustifolia Mgf-Dbg. and Galium corrudifoliumVill. (all found in 24 plots).

Four main groups of plots were delineated usingthe cluster analysis: garigues (6 plots), with anaverage of 6.0 species per plot; woodlands (5 plots),with 8.4 species per plot on average; shrubland (7plots), floristically very similar to the grasslandsand an average of 8.6 species per plot; grasslands,which were the most common and differentiatedgroup (32 plots) as well as that with the highestspecies richness per plot (10.7 on average).

Indirect gradient analysis

The first two axes of CA explained approximatelyone-fifth of the total variance in the species data(Table 2). The first CA axis (Fig. 1) separated outthe woodland plots from the others. Although agradient was evident, these plots were scattered ina reduced amount of the ordination space. Cruci-

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ata glabra (L.) Ehrend. and Carex flacca Schreberwere associated with the four woodland plots (alsowith a high score along the second axis), whereasErica arborea L. was associated with the otherwoodland plot. The spreading of the other specieswas compressed in the ordination space and canhardly be distinguished.

To achieve a better scatter of the non-woodlandplots, a second CA ordination was done afterremoving the five woodland plots (Fig. 2;Table 2).A gradient from the plots sampled on rocky sitesto dry grasslands and then to Juniperus oxycedrus L.ssp. oxycedrus and Fraxinus ornus shrubland is rec-ognizable from the left to the right hands of theCA biplot. Sedum rupestre L., Stachys recta L. ssp. ser-pentini (Fiori) Arrig., Silene paradoxa L., Helichrysumitalicum (Roth) Don, Festuca inops De Not., Alyssumbertoloni Desv., Plantago holosteum Scop. and Stipaetrusca were associated with the rocky sites(garigues). In the middle and lower part of theordination diagram the plots sampled in slightlydifferent types of grassland can be observed; thespecies associated with these plots were Triniaglauca (L.) Dumort., Carex humilis, Galium corrud-

ifolium, Bromus erectus, Danthonia alpina Vest andGenista januensis Viv. Further along the gradientmore developed grasslands can be found; thesewere characterized by Centaurea bracteata Scop.,Festuca robustifolia, Thymus acicularis Waldst. etKit. var. ophioliticus Lacaita and are transitionalbetween xerophile grassland and shrubland com-munities. The plots sampled in the Juniperus oxyce-drus, Erica scoparia L. and Fraxinus ornus shrublandare located in the final part of the gradient, as wellas the species linked to woodland habitat (Rubusulmifolius Schott, Teucrium chamaedrys L.), sciophileconditions (Leucanthemum pachyphyllum Marchi etIlluminati) or more developed soils (Sanguisoirbaminor Scop.).

Direct gradient analysis

The biplot originated by the first two axes of theCCA Fig. 3;Table 2) showed a spread of plotssimilar to that observed in the CA, indicating thatthe most important variables controlling speciesdistribution were included in the survey. A Mon-te-Carlo permutation test indicated that all the

Table 2 Ordination results of gradient analyses using only species cover data (CA) or species cover data constrainedon environmental data (CCA)

Analysis and data Axis 1 Axis 2 Axis 3 Axis 4

CA full data set (50 plots)Eigenvalues 0.568 0.432 0.377 0.319Cumulative percentage of variance 10.9 19.3 26.5 32.7(Sum of all unconstrained eigenvalues = 5.190)

CA reduced data set (45 plots)Eigenvalues 0.478 0.375 0.268 0.245Cumulative percentage of variance 12.4 22.1 29.1 35.4(Sum of all unconstrained eigenvalues = 3.860)

CCA full data set (50 plots)Eigenvalues 0.504 0.305 0.213 0.149Species–environment correlations 0.960 0.911 0.823 0.821Cumulative percentage of variance of species data 9.7 15.6 19.7 22.6Cumulative percentage of species–environment relation 28.1 45.0 56.9 65.2(Sum of all unconstrained eigenvalues = 1.797)

CCA reduced data set (45 plots)Eigenvalues 0.377 0.226 0.136 0.120Species–environment correlations 0.919 0.843 0.843 0.806Cumulative percentage of variance of species data 9.8 15.6 19.1 22.3Cumulative percentage of species–environment relation 29.0 46.4 56.9 66.2(Sum of all unconstrained eigenvalues = 1.298)

632 A. Chiarucci et al.

canonical axes were significant (P value = 0.001).The woodland plots, observed as a separate groupalong the first CA axis, was still well segregated inthe right higher corner, whereas all the other plotsare located in a small region of the ordinationdiagram. The factor most related to these five plots was the dummy variable ‘slope ultramaficdebris’ (Table 3), indicating that the parent mate-rial on which they were set significantly affectedthe floristic features of these plots. Other variables

most related to the first two axes of CCA ordina-tion were tree cover, rockiness, exchangeablecalcium, total nitrogen and carbon/nitrogen ratio(Table 3). The third axis (not shown) was mostlyrelated to the amount of exchangeable nickel.

As done with the CA, we performed a separateCCA after the removal of the woodland plots asso-ciated with the slope debris. The resulting scatterplot (Fig. 4; Table 2) showed a gradient from thegarigues to dry grasslands and then to the Junipe-

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Fig. 1. Biplot of the first two axes of the correspondence analysis, showing sites and species of the complete dataset (50 plots). Plots are labeled as follows: circles: garigues, triangles: grassland, squares: shrubland, crosses: wood-lands. Species are labeled as follows: Aly ber: Alyssum bertolonii; Aly mon: Alyssum montanum; Bra rup: Brachypodiumrupestre; Bro ere: Bromus erectus; Car fla: Carex flacca; Car hum: Carex humilis; Cen bra: Centaurea bracteata; Cha hir:Chamaecitisus hirsutus; Chr gry: Chrysopogon gryllus; Cru gla: Cruciata glabra; Dan alp: Danthonia alpina; Eri arb: Ericaarborea; Eri sco: Erica scoparia; Fes ino: Festuca inops; Fes rob: Festuca robustifolia; Fil vul: Filipendula vulgaris; Fra orn:Fraxinus ornus; Gal cor: Galium corrudifolium; Gen jan: Genista januensis; Hel ita: Helichrysum italicum; June oxy: Junipe-rus oxycedrus ssp. oxycedrus; Leu pac: Leucanthemum pachyphyllum; Lin try: Linum tryginum; Pin pin: Pinus pinaster; Plahol: Plantago holosteum; Rub ulm: Rubus ulmifolius; San min: Sanguisorba minor; Sed rup: Sedum rupestre; Sil par: Sileneparadoxa; Sta rec: Stachys recta; Sti etr: Stipa etrusca; Sti tir: Stipa tirsa; Teu cha: Teucrium chamaedris; Thy aci: Thymusacicularis var. ophioliticus; Tri gla: Trinia glauca.

Vegetation–environment relationships 633

rus oxycedrus, Erica scoparia and Fraxinus ornusshrublands, analogous to the pattern observed inthe CA (although reversed along the first axis). AMonte-Carlo permutation test indicated that allthe canonical axes were significant (P value =0.001). The first axis was positively related to therockiness and pH and negatively to the tree cover,the exchangeable calcium in the soil and the C/Nratio. There was a significant positive relation

between the second axis and the concentration ofexchangeable nickel and elevation. The third axis(not shown) was related to solar irradiance. Ingeneral, it appears from the CCA ordination thatthe vegetation pattern, from the garigues to grass-land and, finally, to shrubland was related (Table4) to two groups of soil variables. The first wasassociated with soil accumulation processes,expressed by the inverse of rockiness, the

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Fig. 2. Biplot of the first two axes of the correspondence analysis, showing sites and species of the reduced dataset (45 plots). Plots are labeled as follows: circles: garigues, triangles: grasslands, squares: shrubland. Species arelabeled as follows: Aly ber: Alyssum bertolonii; Aly mon: Alyssum montanum; Art alb: Artemisia alba; Bra rup: Brachy-podium rupestre; Bro ere: Bromus erectus; Car hum: Carex humilis; Cen bra: Centaurea bracteata; Chr gry: Chrysopogongryllus; Dan alp: Danthonia alpina; Dor hir: Dorycnium hirsutum; Eri sco: Erica scoparia; Fes ino: Festuca inops; Fes rob:Festuca robustifolia; Fil vul: Filipendula vulgaris; Fra orn: Fraxinus ornus; Gal cor: Galium corrudifolium; Gen jan: Genistajanuensis; Hel ita: Helichrysum italicum; June oxy: Juniperus oxycedrus ssp. oxycedrus; Leu pac: Leucanthemum pachyphyl-lum; Lin try: Linum tryginum; Peu cer: Peucedanum cerviaria; Pla hol: Plantago holosteum; Pot hir: Potentilla hirta; Rubulm: Rubus ulmifolius; San min: Sanguisorba minor; Sed rup: Sedum rupestre; Sil par: Silene paradoxa; Sta rec: Stachys recta;Sti etr: Stipa etrusca; Sti tir: Stipa tirsa; Teu cha: Teucrium chamaedris; Thy aci: Thymus acicularis var. ophioliticus; Trigla: Trinia glauca.

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exchangeable calcium content (typically low inserpentine soils), the C/N ratio and the total nitro-gen content. The second group was associated withexchangeable nickel in the soil and elevation. Inaddition, the grassland plots where Brachypodiumrupestre (Host.) R. et S. and Danthonia alpina dom-inate were related to the percentage of tree coverand the C/N ratio. The cover of the planted pineswas significantly correlated to total tree cover (rs

= 0.816, P < 0.001), confirming the importanceof pine canopies in controlling understorey vegetation.

DISCUSSION

The results emerging from indirect and directordinations were almost identical, confirming that

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Fig. 3. Species conditional triplot based on canonical correspondence analysis of the complete data set (50 plots).Plots are labeled as follows: circles: garigues, triangles: grasslands, squares: shrubland, crosses: woodlands. Speciesare labeled as follows: Aly mon: Alyssum montanum; Bra rup: Brachypodium rupestre; Bro ere: Bromus erectus; Car fla:Carex flacca; Car hum: Carex humilis; Cen bra: Centaurea bracteata; Cha hir: Chamaecitisus hirsutus; Chr gry: Chryso-pogon gryllus; Cru gla: Cruciata glabra; Dan alp: Danthonia alpina; Eri arb: Erica arborea; Eri sco: Erica scoparia; Fesino: Festuca inops; Fes rob: Festuca robustifolia; Fil vul: Filipendula vulgaris; Fra orn: Fraxinus ornus; Gal cor: Galiumcorrudifolium; Gen jan: Genista januensis; Hel ita: Helichrysum italicum; June oxy: Juniperus oxycedrus ssp. oxycedrus; Leupac: Leucanthemum pachyphyllum; Lin try: Linum tryginum; Pla hol: Plantago holosteum; Sco aus: Scorzonera austriaca; Sedrup: Sedum rupestre; Sil par: Silene paradoxa; Sta rec: Stachys recta; Sti etr: Stipa etrusca; Sti tir: Stipa tirsa; Teu cha: Teu-crium chamaedris; Thy aci: Thymus acicularis var. ophioliticus; Tri gla: Trinia glauca. Quantitative environmental vari-ables are reported as solid arrows; the dummy variable ‘Debris’ is reported as an asterisk.

Vegetation–environment relationships 635

the most important environmental variables con-trolling species distribution were taken intoaccount in this survey. Nevertheless, the propor-tion of variance explained by the constrained ordi-nation was not high. This is comparable with thefindings of other studies using similar approachesand indicates that there is a consistent amount ofstochastic variation in the distribution of species.

Substrate setting (slope debris vs outcroppingrocks) was found to be the main factor associatedwith the presence of a forest community domi-nated by Quercus cerris. For the ultramafic outcropsof Tuscany, the limited presence of woody plantcommunities in ‘sites with sufficiently deep soil’has already been reported by Messeri (1936). PichiSermolli (1948) stated that, in the Upper TiberValley, woodlands do not develop directly fromtypical ultramafic plant communities, but rathertheir presence is limited to sites close to otherlithological substrates or on slope debris, wherethe soil is more developed. The observations ofthese authors were confirmed by the findings of thepresent test.

The exchangeable fraction of potentially toxicmetals, such as chromium and cobalt was found tobe too low to affect plant life (Brooks 1987; Robin-son et al. 1996), whereas nickel was found to bepresent at levels comparable to other surveys of

Tuscan ultramafic soils (Pandolfini & Pancaro1992; Chiarucci et al. 1998a, 1998b). The amountof exchangeable nickel in the soil was not corre-lated to soil pH (rs = 0.056, P = 0.723), in con-trast with previous studies for Tuscany (Chiarucciet al. 1998a, 1998b) and other parts of the world(e.g. Robinson et al. 1996). This lack of correlationbetween soil pH and exchangeable nickel may bepartially due to the low range of the pH present atthis site and the consequent reduced spreading ofthe relation. Although the main gradient of vege-tation dynamics (from garigues to grasslands andthen to shrubland) was unrelated to the nickelcontent of the soil, some plots sampled in Stipatirsa grasslands were significantly associated withsoil nickel content. Therefore, nickel may have animportant role in some environmental situations,where its negative influence is compounded bynutrient deficiency and soil drought preventingvegetation succession from grassland to woodycommunities. The hypothesis that metal contentis not the most important limiting factor causingthe infertility of Tuscan serpentine soils hasrecently been advanced in both pedological(Angelone et al. 1991, 1993) and vegetation(Chiarucci & De Dominicis 1995; Chiarucci et al.1996, 1998a, 1998b) studies. These studies wereat variance with previous reports, and these

Table 3 Inter set correlations of environmental variables with axes of the canonical correspondenceanalysis (CCA) performed with the complete data set(50 plots)

Environmentalvariable Axis 1 Axis 2 Axis 3 Axis 4

Elevation -0.281 -0.215 0.312 -0.042Slope 0.082 0.285 -0.018 -0.358Tree cover 0.656 -0.227 0.105 -0.102Rockiness -0.537 0.648 -0.148 -0.123Ni -0.140 -0.163 0.689 -0.104Ca 0.563 -0.248 -0.122 0.091Mg/Ca -0.310 0.141 0.101 -0.039N tot 0.368 -0.087 -0.189 -0.359C/N 0.178 -0.366 0.069 0.094pH -0.324 0.220 0.048 0.079Solar irradiance -0.110 0.017 0.122 -0.382Debris 0.814 0.439 0.093 0.058

For variables abbreviations see Table 1.

Table 4 Inter set correlations of environmental variables with axes of the canonical correspondenceanalysis (CCA) performed with the reduced data set (45 plots)

Environmentalvariable Axis 1 Axis 2 Axis 3 Axis 4

Elevation -0.023 0.399 0.334 0.172Slope 0.163 -0.165 0.077 -0.311Tree cover -0.596 0.069 -0.138 -0.303Rockiness 0.794 -0.265 0.061 -0.054Ni -0.003 0.683 -0.006 -0.241Ca -0.564 -0.012 0.004 0.052Mg/Ca 0.280 0.038 -0.243 -0.285N total -0.325 -0.242 0.225 0.044C/N -0.402 0.153 -0.026 -0.017pH 0.342 -0.001 -0.035 -0.404Solar irradiance 0.066 0.089 0.357 -0.321

For variables abbreviations see Table 1.

636 A. Chiarucci et al.

authors emphasized the importance of the metalfraction available to plants as opposed to totalmetal concentrations. The limited importance of soil metal toxicity in controlling ultramafic vegetation has also been reported for other sites:Carter et al. (1987) did not find any evidence thatnickel was the cause of the serpentine infertility inthe keen of Hamar, Shetland Islands. Kruckeberg

(1992) noted that cobalt, chromium, iron andnickel did not affect plant growth in the ultra-mafics of western North America. In New Zealand,Lee (1992) observed that only in some southernultramafics is nickel likely to reduce plant growth.In a review, Proctor and Nagy (1992) stated thatmany assumptions about causal roles of nickel indetermining the unusual serpentine vegetation are

2423

22

2120

1918

1716

15

1413 12

1110 98

7

6

5

4

321

-0.9 +1.1

-0.9

+0.9

Rockyness

Tree cover

Ca

C/N

pH

N tot

Mg/Ca

Slope

Solar Irradiance

Elevation

Ni

Sed rup

Sil par

Art alb

Sta rec

24. Fes ino

13. Fra orn

23. Hel ita

14. Eri sco

22. Pla hol

10. Fil vul

20. Aly mon

17. Lin try

18. Sti etr

7. Cha hir

19. Chr gry

12. Jun oxy

8. Leu pac

Bra rup

11. Teu cha

21. Sco aus

15. Tri gla

Aly ber

5. Thy aci

16. Gen jan

Pot hir

Sti tirCen bra

9. San min

6. Peu cer

Dor hir

4. Car hum

3. Dan alp

Gal cor

2. Bro ere

1. Fes rob

Fig. 4. Species conditional triplot based on canonical correspondence analysis of the reduced data set (45 plots).Plots are labeled as follows: circles: garigues, triangles: grassland, squares: shrubland. Species are labeled as follows:Aly ber: Alyssum bertolonii; Aly mon: Alyssum montanum; Art alb: Artemisia alba; Bra rup: Brachypodium rupestre; Broere: Bromus erectus; Car hum: Carex humilis; Cen bra: Centaurea bracteata; Cha hir: Chamaecitisus hirsutus; Chr gry:Chrysopogon gryllus; Dan alp: Danthonia alpina; Dor hir: Dorycnium hirsutum; Eri sco: Erica scoparia; Fes ino: Festucainops; Fes rob: Festuca robustifolia; Fil vul: Filipendula vulgaris; Fra orn: Fraxinus ornus; Gal cor: Galium corrudifolium;Gen jan: Genista januensis; Hel ita: Helichrysum italicum; June oxy: Juniperus oxycedrus ssp. oxycedrus; Leu pac: Leucan-themum pachyphyllum; Lin try: Linum tryginum; Peu cer: Peucedanum cerviaria; Pla hol: Plantago holosteum; Pot hir: Poten-tilla hirta; San min: Sanguisorba minor; Sco aus: Scorzonera austriaca; Sed rup: Sedum rupestre; Sil par: Silene paradoxa;Sta rec: Stachys recta; Sti etr: Stipa etrusca; Sti tir: Stipa tirsa; Teu cha: Teucrium chamaedris; Thy aci: Thymus acicularisvar. ophioliticus; Tri gla: Trinia glauca. Quantitative environmental variables are reported as solid arrows.

Vegetation–environment relationships 637

unfounded and often disproved by more in-depthstudies.

The positive relation between Stipa tirsa grass-lands and the most elevated sites confirmed thattopographic position is an important factor in con-trolling vegetation patterns in this semiaridhabitat. Site drought, due to aspect and slope hasbeen shown to be one of the most important factorsin controlling the vegetation of Tuscan ultramaficsoils (Chiarucci et al. 1998b). Although a signifi-cant relation was found between solar irradianceand the third CCA axis using the reduced data set,slope and solar irradiance were not found to be themost important factors in controlling vegetationcomposition. However, 43 out of 50 plots weresampled in the southern half of the possibledegrees of aspect and none was in the most north-ern 90°, reducing the possibilities of testing aspectinfluence. Elevation, which is important in con-trolling soil accumulation and development, wasone of the most significant factors, confirming theimportance of topographic features in controllingvegetation pattern.

Tree cover, mostly due to the planted pines, sig-nificantly affected the constrained ordination of the plots. The effects of pine plantations on theunderstorey vegetation have previously beenstudied in other ultramafic outcrops of Tuscany(Chiarucci & De Dominicis 1995; Chiarucci et al.1996, 1998a). These studies showed that pineplantation strongly modified the species com-position and abundance of the understorey plantcommunities by allowing the spread of grasslandspecies and reducing endemic species typical ofgarigue habitats. In the constrained ordinationusing the reduced data set (Fig. 4), tree cover wasalmost at the extreme opposite with respect to thegarigue plots and endemic species. However, thepositive relation between pine cover and soil nickel observed in previous studies was not confirmed, with the two factors being unrelated.This lack of agreement could be due to the lowrange of soil pH observed in the present study.Although the area was relatively homogeneous,different vegetation stages were observed and alarger range of soil pH was expected. In the ultramafic outcrop near Pievescola, covering a sim-ilarly large area, Chiarucci and De Dominicis(1995) found a similar range of pH, but a stronger

relationship between pH and the exchangeablenickel.

The plots related to the tree cover in the con-strained ordination also had a higher C/N ratio, alower pH and a higher content of exchangeablecalcium. These factors and others may be consid-ered as factors influencing vegetation compositionand structure, as well as the effects of vegetationprocesses, through complex interactions of feed-back mechanisms. The development of a moreclosed canopy cover by woody communities pro-motes accumulation of an organic layer in the soil,improving soil fertility and inducing a better plant growth by positive feedback. The naturaldynamics of the system towards a woody vegeta-tion led to the disappearance of endemic species,adapted to the less evolved soils, and the spread ofthe more competitive species. This implies thatpersistence of the garigue communities, in whichthe serpentine endemic species grow (Pichi Sermolli 1948; Pignatti 1982; Arrigoni et al.1983; Chiarucci et al. 1995), may be closely linkedto particular conditions, such as steep slopes, ormay require periodic disturbances, which preventsuccession to the taller vegetation. Pine planta-tion represents a significant factor controlling vegetation composition and removal is needed toensure the conservation of the ultramafic garigues.Stipa tirsa grasslands are still present in higherlocations, where site drought, and a higherexchangeable nickel content in the soil, preventvegetation dynamics and limit pine colonization.However, the area with the combination of topo-graphic features required to prevent pine coloniza-tion is too small to ensure the continuation of thesewell-structured Stipa tirsa grasslands. If conserva-tion of this rare habitat is to be ensured, removalof the introduced pines should be considered a priority.

ACKNOWLEDGEMENTS

The authors warmly acknowledge Dr A. Doderofor help in chemical analyses of soils, Dr A.Gabellini, Mr V. Gonnelli for help during fieldsampling. The project was supported by theProvincial Administration of Arezzo. Specialthanks is due to B. J. Anderson and two anony-

638 A. Chiarucci et al.

mous reviewers for comments on an earlier versionof the manuscript.

REFERENCES

Angelone M., Vaselli O., B ini C. & Coradossi N. (1993) Pedogeochimical evolu-tion and trace elements availability to plants inophiolitic soils. Science of the Total Environment 129:291–309.

Angelone M., Vaselli O., B ini C., Coradossi N. & Pancani M. G. (1991) Totaland EDTA-extracTable element contents in ophi-olitic soils from Tuscany (Italy). Zeitung Pflanzen-ernäharung Bodenkunde 154: 217–223.

Arrigoni P. V., R icceri C. & Mazzanti A.(1983) La Vegetazione serpentinicola del Monte Ferratodi Prato in Toscana. Centro di Scienze Naturali,Prato.

Baker A. J. M., Proctor J. & Reeves R. D.(1992) The Vegetation of Ultramafic (Serpentine) Soils.Kluwer Academic Publishers, Dordrecht.

Bartorelli U. (1967) Tavole numeriche dell’as-solazione annua. Annuali dell’Accademia Italiana diScienze Forestali 16: 61–83.

B igi L. & Rustici L. (1984) Regime idrico dei suolie tipi climatici in Toscana. Dipartimento Agricolturae Foreste, Regione Toscana.

Brooks R. R. (1987) Serpentine and its Vegetation. AMutlidisciplinary Approach. Croom-Helm, Londonand Sydney.

Carter S. P., Proctor J. & Slingsby D. R.(1987) Soil and vegetation of the Keen of Hamarserpentine, Shetland. Journal of Ecology 75: 21–42.

Chiarucci A., Bonini I., Gonnelli V. & De Dominicis V. (1996) The Stipa tirsa communities of the Upper Tiber Valley, Italy andtheir conservation. Colloques Phytosociologiques 24:305–309.

Chiarucci A. & De Dominicis V. (1995)Effects of pine plantations on ultramafic vegeta-tion of Central Italy. Israel Journal of Plant Sciences43: 7–20.

Chiarucci A., Foggi B. & Selvi F. (1995)Garigue plant communities of ultramafic outcropsof Tuscany (Central Italy). Webbia 49: 179–192.

Chiarucci A., R iccucci M., Celesti C. & DeDominicis V. (1998a) Vegetation-environmentrelationships in the ultramafic area of MonteFerrato, Italy. Israel Journal of Plant Sciences 46:213–221.

Chiarucci A., Robinson B. H., Bonini I.,Petit D., Brooks R. R. & De Dominicis V.(1998b) Vegetation of Tuscan ultramafic soils inrelation to edaphic and physical factors. FoliaGeobotanica 33: 113–131.

Kruckeberg A. R. (1992) Plant life of westernNorth American ultramafics. In: The Ecology ofAreas with Serpentinized Rocks. A World View. (edsB. A. Roberts & J. Proctor) pp. 31–74. KluwerAcadaemic Publishers, The Netherlands.

Lee W. G. (1992) The serpentinized areas of NewZealand, their structure and ecology. In: TheEcology of Areas with Serpentinized Rocks. A WorldView. (eds B. A. Roberts & J. Proctor) pp.375–417. Kluwer Acadaemic Publishers, TheNetherlands.

Messeri A. (1936) Ricerche sulla vegetazione deidintorni di Firenze. IV. La vegetazione delle rocceofiolitiche del Monte Ferrato (presso Prato). NuovoGiornale Botanico Italiano 43: 277–372.

Moore P. D. & Chapman S. B. (1986) Methods inPlant Ecology. Blackwell Scientific Publications,Oxford.

Moraldo B. (1986) Il genere Stipa L. (Gramineae)in Italia. Webbia 40 (2): 203–278.

Palmer M. W. (1993) Putting things in evenbetter order. The advantages of Canonical Corre-spondence Analysis. Ecology 74 (8): 2215–2230.

Pandolfini T. & Pancaro L. (1992) Biogeo-chemical Survey of some Ophiolitic Outcrops inTuscany. Flora 187: 341–351.

P ichi Sermolli R. (1948) Flora e vegetazionedelle Serpentine e delle altre ofioliti dell’alta valledel Tevere (Toscana). Istituto botanico dell’Uni-versità di Firenze. Webbia 6: 1–380.

P ignatti S. (1982). Flora D’italia. Vol. 1–3. Eda-gricole, Bologna.

Podani J. (1993). SYN-TAX, Version 5.0. User’sGuide. Scientia Publishing, Budapest.

Podani J. (1994) Multivariate data analysis in ecologyand systematics. A Methodological Guide to the SYN-TAX 5. 0 Package. SPB Academic Publishing bv,The Hague.

Proctor J. & Nagy L. (1992) Ultramafic rocksand their vegetation: an overview. In: The Vegeta-tion of Ultramafic (Serpentine) Soils. (eds A. J. M.Baker, J. Proctor & R. D. Reeves) pp. 469–494. Kluwer Academic Publishers, Dordrecht.

Roberts B. A. & Proctor J. (1992) The Ecology of Areas with Serpentinized Rocks. A WorldView. Kluwer Acadaemic Publishers, The Nether-lands.

Robinson. B. H., Brooks R. R., K irkmanJ. H., Gregg P. E. H. & Gremigni P. (1996)

Vegetation–environment relationships 639

Plant-available elements in soils and their influ-ence on the vegetation over ultramafic (‘Serpen-tine’) rocks in New Zealand. Journal of The RoyalSociety of New Zealand 26: 457–458.

ter Braak C. J. F. (1986) Canonical correspon-dence analysis: a new eigenvector technique formultivariate direct gradient analysis. Ecology 67(5): 1167–1179.

ter Braak C. J. F. (1987) The analysis of vegeta-tion-environmente relationships by Canonical cor-respondence analysis. Vegetatio 69: 69–77.

ter Braak C. J. F. (1988) CANOCO. A FORTRANprogram for canonical community ordination by[ partial] [detrended] [canonical] correspondence analy-sis, principal component analysis and redundancyanalysis [Version 2.1]. Upgrade To Version 3.1. GLW,Wageningen.

ter Braak C. J. F. & Smilauer P. (1998) CANOCO

Reference manual and user’s guide to Canoco forWindows: Software for Canonical Community Ordina-tion (Version 4). Microcomputer Power, Ithaca,New York.