Journal of Geochemical Exploration 132 (2013) 34–40

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Journal of Geochemical Exploration

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Geochemical approach to assessing human impacts in Cork Oak forestsoils of the MED region

Iain McLellan a, Andrew Hursthouse a,⁎, Adélia Varela b,c, Cristina Silva Pereira b,d

a Institute of Biomedical & Environmental Health Research, School of Science, University of the West of Scotland, Paisley, UKb Instituto de Biologia Experimental e Tecnológica, IBET, Oeiras, Portugalc Instituto Nacional de Recursos Biológicos, INRB, Oeiras, Portugald Instituto de Tecnologia Química e Biológica – Universidade Nova de Lisboa, ITQB-UNL, Oeiras, Portugal

⁎ Corresponding author. Tel.: +44 141 848 3213.E-mail address: andrew.hursthouse@uws.ac.uk (A. H

0375-6742/$ – see front matter © 2013 Elsevier B.V. Allhttp://dx.doi.org/10.1016/j.gexplo.2013.04.005

a b s t r a c t

a r t i c l e i n f o

Article history:Received 1 November 2012Accepted 20 April 2013Available online 28 April 2013

Keywords:Forest managementNATO Science for PeacePotentially toxic elements (PTEs)Principle component analysis (PCA)Soil geochemistryCork oak

Cork oak forests in theMED region are of critical social and ecological value and under stress from both environ-mental change and variation inmanagement practice. This study, as part of a NATO Science for Peace project (SfP981674), evaluated geochemical methods to assess potentially toxic element (PTE) input to field sites fromQuercus suber forests in Tabarka (Tunisia) and an experimental control forest managed by ARGIS-Sardega(Italy). Surface and subsurface soil samples were collected from environmentally comparable sites in the two re-gions. Sites were identified with varied stand and shrub density and characterised for basic soil properties andmulti element content. The data were evaluated using principle component analysis (PCA) and soil enrichmentfactors (SEF). Soils showed chemical variability associated with differences in parent material and the effects ofbiomass density. Contributions fromevents such asfire damage, aswell as biomass, sea salt and geogenic sourcescan be identified, and evaluation is enhanced using a combination of assessment methods. Ultimately despiteconcerns for the regulation of forest activities in the study area, little direct anthropogenic impact was on soilquality.

© 2013 Elsevier B.V. All rights reserved.

1. Introduction

Forest canopies are important resources of nutrients to surface soilswhilst acting as filters for anthropogenic contaminants. Generally, con-centrations of potentially toxic elements (PTEs) are increased in forestssoils compared to non-forested soils (Nikonov et al., 2001; Yelpatyevskyet al., 1995). The influence of anthropogenic against pedogenic sources ofPTEs in surface soils has previously been investigated using various statis-tical techniques, such as principal component analysis (PCA) (Madridet al., 2006; Rodrigues et al., 2009) or the soil enrichment factor (SEF)(Facchinelli et al., 2001; Kříbek et al., 2010). However, determining aPTE source in soils from these ecosystems is difficult due to (i) bioturba-tion andmixingwith the surfacemineral layer, (ii) elemental cycling dueto root uptake, litter fall and subsequent organicmatter decay, (iii) atmo-spheric transport frommarine and other natural sources and (iv) atmo-spheric transport from local and distant sources (Steinnes and Friedland,2006). It has been suggested that in e.g. urban soils, human inputs can beidentified from the presence of a ‘triad’ of contaminant elements such asCu, Pb and Zn (Madrid et al., 2006; Rodrigues et al., 2009). However in aseparate study, Cu and Zn were identified as being lithogenic in natureand Pb, Hg and Cd having a more anthropogenic signal in industriallycontaminated soils (Borůvka et al., 2005). This emphasises the variability

ursthouse).

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of both natural and anthropogenic signals in surface soils and the needfor methodologies which help in the assessment of management pro-cesses. For many forest ecosystems, studies of within forest cycling ofnatural and anthropogenic substances are of critical concern in particularfor ecosystemswere a potential tension exists betweennatural andman-aged environments.

The Kyoto Protocol calls for the “protection of sustainable forestmanagement practices” and the “protection and enhancement of sinksand reservoirs of greenhouse gases” with forest ecosystems being animportant mitigation aspect of climate change (United Nations, 1998).This is particularly relevant when forest management procedures areused to maintain, or enhance, the contribution of existing forests(Broadmeadow and Matthews, 2003; Dixon et al., 1994). Geograph-ically localised forests, and in particular those which contain speciesthat grow in a narrow range of environmental conditions, such asMediterranean tree species including Quercus suber, are sensitiveto climate change (Resco de Dios et al., 2007; Schröter et al., 2005).The cork oak forests rely on specific agro-forestry management ac-tivities to create a suitable environment (Oliveira and Costa, 2012)and can be found on the coastline of Portugal, Spain, France, Italy,Tunisia, Algeria and Morocco (Mazzoleni et al., 2005).

The development of Q. suber forests as viable economic activitieshas increased since the 19th century to the detriment of other treespecies (Urbieta et al., 2008). The total value of cork products is esti-mated to be €1.1 billion annually (UNAC, 2008) and cork stoppers are

35I. McLellan et al. / Journal of Geochemical Exploration 132 (2013) 34–40

theprimary product ofQ. suber forests. In addition, due to their economicimportance forests containing primarily Q. suber trees are man-made(Foster et al., 2002; Urbieta et al., 2008). It has been estimated that 11%of the Tunisian population live in, or near, forests with 1 million peoplerelying on forests as their primary source of income (Ben Mansouraet al., 2001). If sustainable management practices are introduced at anearly stage, forests are protected which can alleviate poverty in areaswhich depend on forest-sourced products as their primary income(Broadmeadow and Matthews, 2003; Patosaari, 2007).

A 2007 report into the sustainability of Sardinian cork forests con-cluded that an effective forest management system should be developedand implemented. Such implementation, with the input of external help,will ensure that woodlands do not disappear and that over-exploitationproblems are addressed whilst maintaining the public's right for accesse.g. to graze animals, etc. (Scotti and Cadoni, 2007). Products from sus-tainably managed wood products help maintain the carbon balance offorests especially if not substituted by other products. This concern hasbeen highlighted in high profile publications (WWF, 2006, 2007)which emphasise the pressures on forest management across di-verse socioeconomic as well as environmental conditions. Simply,this forestry sector is part of a balance between the efforts in tradi-tional cork producing regions of the EU, which are managed to main-tain FSC accreditation, restricting human interaction and ecosystemdiversity versus those forests in northern Africa where rapid exploi-tation and increased resource demands from subsistence populationsintroduces management processes which threaten production qualityand sustainability (Oliveira and Costa, 2012), including the applicationof priority contaminant materials.

We describe here output from a comparative study of soils from twocontrasting regulatory environments and history of cork exploitation.The aim was to evaluate soil geochemical characterisation methods astools to support the assessment anthropogenic inputs as well as naturalcyclingmechanisms. The sites usedwere ecologically comparable standsin regulated locations in Sardina, Italy and Tabarka, Tunisia, managedunder very different local conditions. The Sardinian sites from withinan experimental and FSC (Forest Stewardship Council) managed loca-tion, the Tunisian sites subject to less formal management protocolsand high informal subsistence population.

2. Soil sampling

Soil samples were collected from three Tunisian Q. suber forestsin February 2009 and from a Sardinian forest in June 2008 andMarch 2009 following international standards (ISO, 2002). Three lo-cations were chosen within each forest and a composite sample wascollected from five sub-samples and sieved to b2 mm in the field(where possible) before transportation to the host laboratory assoon as possible following collection, refrigeration and separation priorto distribution of ~200 g aliquots by courier to the other laboratories.Samples collected were from 0 to 10 cm (SF) and 10–20 cm (SB) from

Table 1Details of sample locations.

Sardinia T

Station Sample GPS co-ordinates Alt. (m) S

North East

1 1 40°54′53.70″ 009°07′52.30″ 460 A2 40°54′53.30″ 009°07′52.10″ 4503 40°54′54.10″ 009°54′54.10″ 455

2 1 40°54′56.10″ 009°07′58.80″ 442 F2 40°54′55.60″ 009°07′59.30″ 4453 40°54′55.30″ 009°08′00.30″ 421

3 1 40°54′48.20″ 009°08′00.50″ 454 R2 40°54′48.80″ 009°08′00.60″ 4513 40°54′48.90″ 009°08′01.50″ 452

the individual sites (n = 18). The sample locations are described inTable 1.

2.1. Sample descriptions

The Sardinian forest has been managed by AGRIS Sardegna since1958 and received Forestry Stewardship Commission certification in1996 (Pintus and Ruiu, 1996). Three separate stations were selectedrepresenting different ecological conditions and age ofQ. suber trees. Sta-tion 1 was destroyed by a fire in 1983 which severely damaged this areaof the forest, approximately 60% of the trees present at the time of thefire were re-cultivated by cutting the tree back to the stump; othertrees were planted and are approximately 25 years old. Station 2 hasQ. suber trees older than 80 years old with some other tree speciespresent. Station 3 has been left at a natural state since 1958 to inves-tigate the effects of the lack of humanmanagement on the ecology ofa Q. suber forest. Q. suber is the principal species in Stations 1 and 2,the principal species in Station 3 is Fraxinus ornus although Q. suberis also present (Pintus and Ruiu, 1996). The underlying geology ofthe Sardinian sample locations is an unequigranular monzogranitepluton from the Carboniferous, Upper Permian period (Pintus andRuiu, 1996). A petrological analysis of a monzogranite on a nearbyisland contains biotite and tourmaline, apatite, zircon, ilmenite andsphene (titanite) as accessory minerals (Innocenti et al., 1997).

The Tunisian samples came from soil profiles developed on marinesediments from the Neogene and Oligocene periods and agrilliceous–sandy–fluviatile sediments from the Triassic period (Shlüter, 2006),specifically—aeolian sand (Ras Rajel), clays and sandstones (AînHamraia)and partially decarbonated limestone and marls (Fej Errih) (Dimanche,1971). The persistent shrub content of the Tunisian forests includesheather (Erica arborea) and common myrtle (Myrtus communis), whichare extensively exploited in this region, with forests of pure cork oak ormixes with some zeen oak, maritime pine and other species such as thepine kernel, pine radiata, pine brutia and eucalyptus.

3. Methods

3.1. Soil physical and chemical properties

Soil pH was determined using the International Standard, namely5 ml soil in 25 ml H2O (ISO, 1994); the Walkely Black method wasused for determining soil organic carbon (Hesse, 1971; Sparks et al.,1996); the hydrometer method for determining particle size (Gee andBauder, 1986); the total N (Kjeldahl method, Bremner and Mulvaney,1982), average value of two independent replicates.

3.2. Soil elemental analysis

A 50 g aliquot of b2 mm soil was sieved to b250 μmusing a stainlesssteel sieve (Endecotts, London). Approximately 2.5 g of the sieved soilwas ground and homogenised using a RM200 mill (Retsch, Germany).

unisia

tation Sample GPS co-ordinates Alt. (m)

North East

în Hamraia 1 36°46′47.50″ 008°51′52.00″ 5472 36°46′49.20″ 008°51′53.80″ 5383 36°46′50.40″ 008°51′50.40″ 548

ej Errih 1 36°46′57.90″ 008°43′47.20″ 7922 36°46′58.30″ 008°43′49.60″ 8043 36°46′58.10″ 008°43′52.70″ 804

as Rajel 1 36°57′14.30″ 008°51′51.50″ 612 36°57′16.20″ 008°51′45.60″ 563 36°57′15.20″ 008°51′48.50′ 48

Table 2Summary chemical composition data for Sardinian cork oak forest soils for two sam-pling periods: multi-element content (mg/kg, dry), OC (organic carbon, weight %);CN (= carbon:nitrogen ratio).

Element Range

2008 2009

Al 31,150–51,119 27,668–45,498Ba 62.4–167.7 45.5–97.9Ca 2851–24,779 2938–13,240Co 6.2–20.2 6.5–15.1Cr 9.4–32.1 9.7–26.9Cu bDL 5.6–20.9Fe 23,107–47,567 15,692–48,899K 1973–2696 1565–2831Li 22.1–36.7 18.7–29.9Mg 4018–11,417 3637–11,620Mn 874–2496 742–1391Na 204–338 210–300Ni 4.8–19.0 4.1–12.5Pb 15.0–33.3 12.6–29.0Sr 11.2–164 11.9–42.0Ti 508–1706 497–1258V 41.6–99.3 28.8–84.2Zn 12.0–117.3 44.6–96.4(OC) 1.85–6.53 2.75–6.80(CN) 8.63–14.00 8.63–14.00

2008: Al, Ba, Ca, Fe, K, Mg, Mn, Na, Ti, V, Zn (n = 18); Sr (n = 15); Li (n = 14); Co(n = 12); Cr, Co (n = 9); Ni (n = 6) detected.2009: Al, Ba, Ca, Fe, K, Mg, Mn, Na, Ti, V, Zn (n = 18); Co, Pb (n = 15); Cr, Li, Sr (n =12); Ni, Cu (n = 6) detected.

36 I. McLellan et al. / Journal of Geochemical Exploration 132 (2013) 34–40

Replicate aliquots of 0.3 g of soil was digested in digestion cups (Envi-ronmental Express, USA) with aqua regia (2 ml HNO3:6 ml HCl; FisherTrace Grade, UK) for 2 h at 92 °C in a hot block (Environmental Express,USA). The samples were made to 50 ml using UHP water and then fil-tered in-situ using a 45 μm filter (FilterMate, Environmental Express,USA). Each batch of samples contained 20 samples: 6 soils digested intriplicate, a procedural blank and a soil certified reference material(CRM) (CRM052-050, LGC Promochem).

Elements were determined by ICP-OES (Perkin Elmer Optima 3000);a calibration series (0 mg/l, 2 mg/l and 10 mg/l of multi-element stan-dard, ME/1001/05; Fisher Scientific, UK) was determined for every 10samples. Each determination was carried out in quadruplicate. Dilutionsweremade if necessary. ICP-OES conditionswere as follows: rf generator:40 MHz, 1.3 kW; Plasma: 1.4 l/min; Auxillary: 0.5 l/min; Nebuliser:0.8 l/min; sample flow rate 1.5 ml/min. A number of wavelengthswere selected for analysis based on their Wohlers value; the mostsuitable wavelength, free of interference, was used for calculation:Al—394.396 nm;Ba—233.519 nm;Ca—315.888 nm;Co—228.611 nm;Cr—267.716 nm; Cu—327.400 nm; Fe—259.936 nm; K—766.494 nm;Li—670.784 nm; Mg—279.074 nm; Na—589.583 nm; Ni—231.606 nm;Pb—220.354 nm; Sr—460.229 nm; Ti—334.937 nm; V—292.407 nm;Zn—206.198 nm.

4. Results & discussion

The detection limits for all elements were in the range 10–20 mg/kg(dry weight soil) with 81% of samples above the detection limit. Usingthe certified reference material (CRM), recoveries for elements werein the range 82–108% with relative standard deviation for all elementsb10%. Using Statistical Package for Social Scientists (SPSS), data waschecked for normal distribution using the Kolmogorov–Smirnov Test.PTE levels were not normally distributed therefore for principal compo-nent analysis log-transformed data and the correlation matrix wereused tominimise the influence of large values; varimax rotated compo-nents with an Eigenvalue > 1 are reported. Outliers were identifiedusing SPSS values which are 1.5 times the inter-quartile range, extremevalues are 3 times the inter-quartile range (SPSS, 2006).

The range of PTE values for the Sardinian samples is listed in Table 2;a number of outliers were identified using box plots (Fig. 1). Tunisiansamples are listed in Table 3, Ras Rajel 1.SF was identified as containinga number of extreme values (Fig. 2) extreme PTE values for Ras Rajel1.SF were: As (1732 mg/kg), Ba (2258 mg/kg), Fe (152,777 mg/kg), K(2442 mg/kg), Mn (7194 mg/kg), Na (187 mg/kg), Pb (1089 mg/kg),Sr (55 mg/kg) and Zn (879 mg/kg). Ras Rajel 2.SF was identified as anextreme value for Ba (893 mg/kg).

5. Discussion

The soil element content shows a contrast in variability betweenthe two locations. The Sardinian samples with a much lower variabil-ity, reflecting the common soil parent material with the higher vari-ability in soils from Tunisian sites highlighting variation in parentmaterial. The relationships of elements associated with human activ-ity, e.g. As, Cd, Co, Cu, Ni, Pb and Zn within principal components(PCs) can suggest negative human impact and influence on soil charac-teristics (Neupane and Roberts, 2009; Nikonov et al., 2001; Rodrigueset al., 2009). Applying PCA to the Sardinian samples (Table 4) did notidentify a unique association of these elements, indicative of anthropo-genic impact. The PCs extracted from the Sardinian Station 1 samplesshow a balance between likely geological inputs (PC 1, 78% of total var-iance) and for PC 2,marine influence (PC 2, 22% of the variance). The PC2 for these samples shows associations between Na, K, Sr and Ca; withCa and Sr strongly correlated (r = 0.989) and are elements commonlyassociated with marine salts (Goldberg et al., 1973), which is expectedgiven site locations. The reduction in individual PC variance in the

subsurface samples at Station 1 suggests mixing of components withinthe soil profile (PC 1—67%, PC 2—33% of the total variation).

This marine signal is not clearly seen in the samples from other Sar-dinian stations and may be due to the younger, less dense canopy inthis area (approximately 25 years old), allowing direct deposition tosoil surface. The more mature trees in other areas of this forest, Station2 and Station 3 (>80 years old) with higher biomass have a widerspread and therefore dilute direct atmospheric deposition. The effectsof the 1983 forest fire, which damaged the original trees in Station 1,may also be identified through disruption of the K:Ca ratio, which isgreater in the surface layer than the sub-surface and not observed at Sta-tion 2 or 3 (i.e. ratio for Station 1 SF—0.55, Station 2 SF—0.30, Station 3 SF—0.44) (Úbeda et al., 2009; Pereira et al., 2011). It is important to notethat organic carbon is associatedwith themain PC in all Sardinian surfacesamples highlighting the importance of leaf decomposition on soil ele-mental levels and forest management protocols (Bauhus et al., 1998)and the sensitivity of the PCA approach to soil chemical inputs.

The three PCs extracted for the sub-surface samples of Stations 2 and3 have almost equal variance (Station 2—35.8, 35.3 and 28.9%, Station 3—35.7, 32.7 and 31.6%) and highlights the absence of strong chemicalinputs, inferring subsurface mixing and weathering processes takingplace in the profile. In the surface layers of these stations the first prin-cipal component has a lower contribution to the % variance (Station 2—51%, Station 3—58%), than for Station 1, but stronger than the subsurface samples. The significance of the 2 or 3 PCs being extracted fromthe Sardinian soil data, and the absence of a single dominating compo-nent, reflects the variability of vegetation, natural weathering processesand soil turnover expected for these locations.

In the case of the Tunisian locations, soil samples also do not re-veal any strong anthropogenic signatures, seen in polluted urbansoils (Rodrigues et al., 2009). Similarly there are no identified an-thropogenic groupings in either the surface or sub-surface depthsof the Tunisian Aîn Hamraia or Fej Errih samples (Table 5), despiteactivities involving uncontrolled solid wastes and chemicals beingobserved from field visits. Themain principal component (PC) extractedfrom the Aîn Hamraia surface samples appears to be associated with thelocal geology (58% of the total variance): with Ca, Mg, Na, Ti and Mn

1e+1

1e+2

1e+3

1e+4

1e+5

1e+6

Al Ca Fe K Mg Mn Na

mg/

kg (

dry)

1

10

100

1000

10000

As Ba Co Cr Pb Sr Ti Zn

mg/

kg (

dry)

a

b

Fig. 2. Box plots of Tunisian soil elemental concentrations a) major elements and b)minor/trace elements. All values mg/kg dry soil.

1e+1

1e+2

1e+3

1e+4

1e+5

1e+6

Al Ca Fe K Mg Mn Na

mg/

kg (

dry)

1

10

100

1000

10000

Ba Co Cr Li Pb Ti Sr Zn

mg/

kg (

dry)

a

b

Fig. 1. Box plots of Sardinian soil elemental concentrations a) major elements and b)minor/trace elements. All values mg/kg dry soil.

37I. McLellan et al. / Journal of Geochemical Exploration 132 (2013) 34–40

associatedwith the sand content, and Al and Fewith the clay content; Feand clay content show significant correlation (r = 0.88, p b 0.05). Theassociation between K and OC in PC 2 (42% of the variance) (r = 0.92,p b 0.01) is clear and relates to biomass inputs (Andivia et al., 2010).The geological influence dominates the sub-surface PCs, with Ca andpH associated in PC 2. The PCs extracted from the Fej Errih samples can

Table 3Summary data for the chemical composition of Tunisiancork oak forest soils sampled in 2009: multi-elementcontent (mg/kg, dry), OC (organic carbon, weight %);CN (= carbon:nitrogen ratio).

Element Range

Al 5214–11,463As 35.4–1732Ba 20.2–2258Ca 420–8834Cr 10.3–17.9Fe 4975–152,777K 648–2442Mg 694–1785Mn 25.7–7194Na 36.4–187Pb 28.5–1089Sr 9.1–55.3Ti 40.0–130.3Zn 21.6–880OC 0.98–5.03CN 14.3–24.58a

Al, Ba, Ca, Fe, K, Mg, Mn, Na (n = 17); Pb, Zn (n = 11);Sr (n = 9); Cr (n = 7); As (n = 5) detected.

a Outlier >80 removed.

also be attributed to the underlying geologywith the texture and organiccarbon accounting for the differences: the surface samples with soil tex-ture indicators (sand, silt and clay) associated with PC 1 (51% of vari-ance) and organic carbon with PC 2 (49% of variance).

The Ras Rajel data were analysed as a pooled data set (0–20 cm),with and without samples removed containing significant elementalcontent outliers and coarse texture (e.g. Ras Rajel 1.SF contained ahigh proportion of stones and pebbles (Φ 2 mm–20 mm) which hadto be discarded before the sample could be sieved to b2 mm in thefield). The PCA revealed in addition to a strong geological PC 1, the influ-ence of pH (associatedwith Ca) or clayminerals (Al and clay texture) inPC 2. In addition,field observations and from satellite imagery of the RasRajel location (Google Earth), show surface disruption associated with(unidentified) mineral exploitation in the vicinity, which may explainthe “exotic” As signal found in some soil samples.

The surface enrichment factor (SEF) as the concentration ratiobetween the upper and lower soil profiles was calculated for all samplelocations ([SF] / [SB]) (see Table 6). The SEF was determined for theSardinian and Tunisian samples separately, but the pattern of relativeassociation for the elements analysed was identical in both locations.The ratio accommodates the background from soil parent materialand highlights surface inputs, and the elements showing little/no frac-tionation (K, Li, Mg, Na, Ti, V) include those that have previously beenidentified through the PCA to show surface inputs from e.g. marine in-fluence, which now appears from the SEF to be balanced by content inthe parent material. Those elements shown to exhibit surface enrich-ment (SEF> > 1) of between 30% and 80%, constitute components as-sociated with both natural and anthropogenic inputs to soils, despitethe latter not being identified in the PCA. It has been widely indicatedthat Pb is a diffuse pollutant, found to be surface enriched in soils

Table 4Varimax rotated principal components identified in multi-element soil data from Sardinian cork oak forests.

Station 1 Station 2 Station 3

Surface Sub-surface Surface Sub-surface Surface Sub-surface

PC 1 PC 2 PC 1 PC 2 PC 1 PC 2 PC 3 PC 1 PC 2 PC 3 PC 1 PC 2 PC 1 PC 2 PC 3

Al 0.998 −0.063 0.952 0.306 −0.049 0.999 −0.014 −0.207 0.879 0.430 −0.998 −0.070 −0.319 0.417 0.851Ca 0.833 0.553 0.718 0.696 −0.112 0.478 0.871 0.947 0.224 −0.230 −0.184 0.983 0.569 −0.294 0.768Fe 0.999 −0.038 0.983 0.183 0.561 0.510 −0.652 −0.766 0.577 0.284 0.438 0.899 −0.063 0.006 0.998K −0.445 0.896 −0.582 −0.813 −0.235 −0.316 0.919 0.586 0.727 −0.358 0.999 −0.032 0.102 0.687 0.719Mg 0.997 −0.083 0.972 0.234 0.814 0.506 −0.285 −0.448 0.578 0.682 −1.000 0.005 −0.912 −0.257 0.319Na −0.217 0.976 −0.183 0.983 −0.942 −0.260 0.212 0.858 0.383 0.343 0.257 −0.966 0.390 0.696 −0.603Ti 0.998 0.058 0.996 0.093 −0.817 −0.355 −0.454 0.273 0.947 0.168 0.998 0.070 0.963 0.152 0.224Ba 0.967 0.254 0.910 0.414 0.998 0.061 0.003 −0.280 0.102 0.954 0.438 0.899 0.304 0.896 0.324Co 0.987 0.160 0.949 0.314 a a 0.641 0.767 0.348 0.846 0.404Cr 0.971 0.239 0.940 0.341 a a 0.070 −0.998 −0.836 −0.535 −0.120Li 0.945 0.327 0.964 0.267 a a 0.899 −0.438 0.853 0.479 −0.207Mn 0.918 0.397 0.894 0.448 0.711 0.690 0.136 −0.479 0.596 0.645 −0.371 0.929 −0.614 0.315 0.724Ni 0.939 0.344 0.910 0.414 a a a aPb 0.939 0.344 0.878 0.478 0.827 0.321 0.461 0.332 0.811 0.483 0.499 0.866 −0.525 0.844 −0.108Sr 0.686 0.728 0.347 0.938 0.988 0.143 −0.063 −0.126 0.290 0.949 −0.640 0.768 0.078 0.027 0.997V 1.000 −0.024 0.987 0.159 0.285 0.958 0.012 0.145 0.988 0.058 0.899 −0.438 0.274 0.743 0.611Zn 0.868 0.497 0.891 0.454 0.567 0.818 −0.098 0.224 0.792 0.568 −0.706 0.708 −0.094 −0.967 −0.236pH 0.755 0.656 0.349 0.937 −0.743 −0.260 −0.617 −0.307 −0.624 −0.719 0.742 −0.670 −0.021 −0.992 0.127OC 0.991 −0.136 0.846 0.533 0.994 −0.027 −0.110 −0.764 −0.258 0.591 −0.942 −0.335 −0.991 −0.133 −0.028Sand −1.000 −0.009 0.803 0.596 −0.763 −0.280 −0.583 0.974 0.226 0.020 −1.000 −0.010 −0.838 −0.055 −0.542Silt 0.918 −0.396 −0.665 −0.746 0.793 0.369 0.484 −0.969 −0.057 0.240 0.999 0.048 0.955 −0.295 0.002Clay 0.510 0.860 0.407 0.913 0.419 −0.200 0.886 0.696 −0.275 −0.663 1.000 −0.031 0.402 0.366 0.840Variance (%) 78.0 22.0 66.5 33.5 50.5 25.3 24.2 35.8 35.3 28.9 58.3 41.7 35.7 32.7 31.6

(a) b detection limits.Values highlighted in bold represent variables that have a strong relationship with each other.

38 I. McLellan et al. / Journal of Geochemical Exploration 132 (2013) 34–40

and peats, through the many decades of leaded petrol in automobiles(Alloway, 1993; Rodrigues et al., 2009) and may indeed be from longrange transport of Pb enriched dusts and associated with enrichmentof Mn, which may also be attributed to vehicle emissions (Boudia etal., 2006). However inputs of Mn have also been related to nutrientleaching from decomposing canopy foliage (Kopáček et al., 2009),with the canopy acting as a filter of airborne dust, not necessarily pol-luted, naturally supplying elements to soil surface horizons. If consid-ered with the PCA results and strong, positive correlation, the surfaceassociation of Ca and Sr are indicative of marine deposition identifiedin the PCA. In the case of Zn, it has been found to be enhanced in soils

Table 5Varimax rotated principal components identified in soil samples from Tunisian cork oak fo

Aîn Hamraia Fej Errih

Surface Sub-surface Surface

PC 1 PC 2 PC 1 PC 2 PC 1 PC 2

Al −0.926 0.378 0.967 0.256 −0.893 0.45Ca 1.000 −0.013 0.278 0.961 0.719 −0Fe −0.971 −0.241 0.973 0.231 −0.978 0.20K 0.328 0.945 0.884 0.467 −0.192 0.98Mg 0.772 0.635 1.000 −0.022 0.111 0.99Na 0.861 0.509 −0.967 −0.256 0.497 0.86Ti 0.926 0.378 −0.993 0.115 −0.244 0.97As a a aBa −0.202 0.979 0.045 −0.999 −0.413 0.91Cr a a aMn 0.998 0.067 0.334 0.943 0.998 0.05Pb a a −0.868 0.49Sr a a aZn a a 0.885 −0pH 0.480 −0.877 −0.699 0.715 0.654 −0OC 0.370 0.929 0.997 −0.072 −0.183 0.98Sand 1.000 0.008 −0.912 −0.410 0.893 0.44Silt −0.754 0.656 1.000 −0.029 −0.997 −0Clay −0.318 −0.948 0.893 0.451 0.769 −0Variance (%) 58.4 41.6 70.6 29.4 51.1 48.9

(a) b detection limits.Values highlighted in bold represent variables that have a strong relationship with each ot

as it is an essential micronutrient and is readily transported fromsub-surface to surface by vegetation (Steinnes and Friedland, 2006),sowithin thriving forest systems, surface enrichment is not unexpected.The situation for Ni, shows only minor surface enrichment, and whilstit may be attributed to anthropogenic sources (Nikonov et al., 2001),it is also commonly found to be enriched due to the influence of localgeology (Neupane and Roberts, 2009), which fits with the minerals as-sociated with the monzogranite (Alloway, 1993). The elements withSEF values b1 (Al, Co, Cr and Fe) indicate lithogenic origin at the sam-pling sites. The association of As with this category confirms a stronggeogenic source for As in this study.

rests.

Ras Rajel

Sub-surface Incl. RR.1.SF Excl. RR.1.SF

PC 1 PC 2 PC 1 PC 2 PC 1 PC 2

1 0.915 −0.404 0.938 −0.303 0.410 0.912.695 0.982 0.187 0.656 0.754 −0.643 −0.7668 0.997 −0.076 0.995 0.099 0.749 0.6621 0.995 0.102 0.998 0.022 0.941 0.3374 0.869 −0.495 0.985 −0.050 0.992 0.1258 0.150 0.989 0.964 0.068 0.909 −0.4180 −0.863 −0.505 0.609 −0.704 0.932 0.362

a 0.993 0.116 0.318 0.9481 0.725 −0.689 0.913 0.362 −0.996 −0.088

a −0.208 −0.978 0.703 0.7119 0.674 0.739 0.979 0.183 0.585 −0.8117 −0.054 −0.999 0.964 0.241 −0.993 0.116

0.984 0.181 0.913 0.403 −0.450 −0.893.465 0.617 0.787 0.957 0.276 −0.978 −0.210.757 0.925 0.381 0.164 0.974 −0.798 −0.6023 −0.650 −0.760 0.866 0.344 0.318 −0.9489 −0.328 0.945 −0.973 0.095 −0.101 −0.995.081 0.206 −0.979 0.994 0.113 0.720 0.694.639 0.280 0.960 0.665 −0.492 0.013 1.000

53.8 46.2 74.8 20.7 52.9 47.1

her.

Table 6Soil enrichment factor (SEF) for elements determined in both Sardinian and Tunisiancork forest soils (pooled data for all sites and sampling campaigns, OC included forcomparison).

Surface Sub-surface None

Element SEF Element SEF Element SEF

Ba 1.31 Al 0.89 K 0.99Ca 1.82 Asa 0.80 Lib 0.98Mn 1.37 Cob 0.91 Mg 0.99Nib 1.07 Cr 0.89 Na 1.03Pb 1.30 Fe 0.92 Ti 0.97Sr 1.81 Vb 0.97Zn 1.27(OC) 1.62

Detected in: (a) Tunisian samples only; (b) Sardinian samples only.

39I. McLellan et al. / Journal of Geochemical Exploration 132 (2013) 34–40

As indicated in the Introduction section study locations in Sardiniaand Tunisia were selected on the basis of comparable forest ecology.Station 1 represents Aîn Hamraia as both sites have little, or no, sur-face vegetation; Station 2 represents Fej Errih as both locations havemore mature trees and a mix of broader vegetation groups withcork trees and Station 3, which has remained unmanaged by theowners of the forest since the 1950s, is similar to Ras Rajel which isovergrown with a variety of native tree and shrub species. The influ-ence of agro-forestry management practices can be more easily com-pared for the three Sardinian locations, close in proximity and withcommon soil parent material. Extending this comparison to theTunisian sites is complicated by varying underlying parent material.However, the PCA and SEF assessment does allow elemental sourcesignatures to be identified in each location, associated with ecosystemproperties. Given the limited number of sample locations, it appearsthat multi-element analysis is sensitive to forest management andecological conditions: the impacts of forestry management procedures,i.e. either leaving an area to return to its natural state or having an areawhere the shrubbery is removed on a regular basis, is reflected in thedifferences seen in the principal components associated with theSardinian sites, with decomposing leaves from different trees speciesreleasing elements at different rates back into the soil.

6. Conclusion

To our knowledge this is the first study to investigate PTE distribu-tion in Q. suber forest soils. Whilst obvious human influence on thesoil elemental levels is not apparent, the style of forest managementdoes have an effect on the elemental distribution within cork forests.It is clear that vegetation and the structure of forest ecosystem signif-icantly influence on element cycling in surface soils, despite the stronginfluence of the local geology. Furthermore the different controls onforest use and accessibility, i.e. private and State-owned, does notappear to have a negative influence on the input of potentiallytoxic elements into surface soils.

The implementation of effective forest management systems, as ex-emplified by the work of AGRIS Sardegna, highlights the importance ofagro-forestry management on the sustainability and preservation ofQ. suber forests. It should be emphasised that without positive humaninfluence, these forests would slowly disappear. The application ofmulti-element analysis and relatively straightforward data analysisprovides a sensitive and robust tool to ensure that the impact ofhuman intervention can be assessed and ecosystemmanagement in-formed at a critical time in the history of the cork forest industry.

Acknowledgments

The authors would like to thank UWS for a PhD studentship forIMcL, NATO Science for Peace (SfP 981674) and FC&T (POCTI/AMB/57374/2004) for part funding this research. Samples were collected

following permission from AGRIS Sardegna, Sardinia and the Sylvo-pastoral Institute of Tabarka, Tunisia. The authors wish to personallyacknowledge Valeria Mazzoleni, Maria Daria Fumi and Elisa Novelli(Università Cattolica del Sacro Cuore, Piacenza, Italy) for their helpwith the sampling operations.

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