Influence of soil mineralogy and chemistry on site quality within geological regions in Sweden

Forest Ecology and Management 170 (2002) 75–88

Influence of soil mineralogy and chemistry on site quality within geological regions in Sweden Johan Stendahla,*, Sven Sna¨llb, Mats T. Olssona, Peter Holmgrenc a

Department of Forest Soils, SLU, P.O. Box 7001, S-750 07 Uppsala, Sweden b Geological Survey of Sweden, P.O. Box 670, S-751 28 Uppsala, Sweden c FAO, Forestry Department, Viale delle terme di Caracalla, 00100 Rome, Italy Received 23 November 2000; received in revised form 15 August 2001; accepted 10 October 2001

Abstract The relationship between soil properties and forest site quality was investigated. The site quality functions currently used fail in predicting variations within regions and the purpose of this study was to evaluate if the local accuracy in forest resource assessments could benefit from the use of geological and geochemical data. The investigation was conducted in mid-Sweden within two geological regions. The mineralogy of the parent material (C horizon) was estimated using a method for normative mineralogical assessment and the soil chemistry was determined for five soil horizons. The importance of individual minerals for site quality was different within the two geological regions. Functional relations were established between the properties in different soil horizons and site index. The functions between mineralogy and site index were improved by splitting the data according to the geologically different regions. The mineralogy explained 37–61% of the variation in site index, whereas the properties in the upper soil profile (O–B horizon) related more strongly to site index (18–80%). Stronger relations could be established in the mineralogically rich than in the mineralogically poor area. # 2002 Elsevier Science B.V. All rights reserved. Keywords: Soil-site study; Soil chemistry; Normative analysis; Forest site index

1. Introduction The abiotic factors influencing forest growth may be divided into those related to: (a) climate (temperature, precipitation, insolation, etc.), (b) topography (including hydrological conditions) and (c) parent material (e.g., soil mineralogy and texture). Their integrated effect gives the conditions for plant growth, but it is difficult to evaluate the contribution from each factor separately since some of them interact (e.g., *

Corresponding author. Tel.: þ46-18-673801; fax: þ46-18-673800. E-mail address: [email protected] (J. Stendahl).

climate and topography). Each factor act at different spatial scale and may be of varying importance depending on the size of the area of interest. In Sweden, there is a strong thermal gradient accounting for a large-scale trend in forest production due to latitude and altitude. Apart from this, there are large-scale gradients in precipitation and N-deposition. If we want to establish nation-wide deterministic functions for potential forest production (site quality classification) based on site properties, they will be dominated by large-scale trends and causal relations between forest productivity and site properties that are general, i.e. that are present under all conditions. In the Swedish system for site quality classification

0378-1127/02/$ – see front matter # 2002 Elsevier Science B.V. All rights reserved. PII: S 0 3 7 8 - 1 1 2 7 ( 0 1 ) 0 0 7 7 8 - 2

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(Ha¨ gglund and Lundmark, 1977), the functions are dominated by latitude, altitude and ground vegetation type, but it has been shown that this system fails to predict local variations in forest production potential (Holmgren, 1994). Since forest planning and management is carried out on relatively small geographic entities there is great interest in providing site quality evaluation with a greater spatial resolution than today. Investigations that relate site-quality to site properties have been considering general environmental conditions such as climatic factors, topographic factors, general soil properties, and soil chemical properties (Lipas, 1985; Jokela et al., 1988; Monserud et al., 1990; Tamminen, 1993; Holmgren, 1994; Corona et al., 1998). In most cases the soil sampling has been concentrated to the organic and uppermost mineral soil horizons, which have been analysed mainly for exchangeable nutrients and pH. The influence of the tree stand on the soil was recognised early by, e.g., Ebermayer and Morozov around the turn of the 20th century (Fischer and Binkley, 2000), and Jenny (1941) identified the vegetation as an important factor in pedogenesis. Investigations, which have aimed at predicting tree nutrient availability from topsoil properties have historically been unsuccessful since the aggradation of forest biomass cannot be fuelled by nutrients from the forest floor that is itself aggrading or in steady state (Fischer and Binkley, 2000). The use of topsoil data thus introduces a problem of cause and effect in the relationship between forest properties and the topsoil, and they are not independent predictors in site quality functions. Also, they will only poorly reflect the influence of the parent material with regard to geochemistry or mineralogy. Weathering of minerals may supply all essential nutrients for forest growth, except nitrogen (Zabowski, 1990), and soil mineralogy is one of the main factors controlling the long-term nutrient availability in the soil (Cole, 1995). Especially, since the deep soil horizons are of considerable importance for forest productivity (Fischer and Binkley, 2000). This study aims at describing the isolated influence of the parent material mineralogy on the forest site quality, while keeping other factors the same. The extensive national databases on soil chemistry, geology and other soil properties have been increasingly applied in environmental monitoring (Selinus, 1996) and planning (Nikkarinen et al., 1996). In forestry, such data may hold valuable information

for forest management. Functions that relate standindependent properties to site quality may help to improve the precision in the site classification systems on a regional scale. The specific objectives in this study were: (a) to predict forest site quality (based on a forest site index) from soil mineralogy of the C horizon, (b) to relate properties of the O, A, E, upper B and lower B horizon to site index, and (c) to compare how the results of (a) and (b) differ between a mineralogically rich and poor region.

2. Material and methods 2.1. The sampling sites The data were collected at 40 sites located within the counties Dalarna and Ha¨ lsingland in Sweden (Fig. 1). When undertaking an investigation of soil-site

Fig. 1. The sampling sites in mid-Sweden.

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relationships it is important to define the area of study with regard to geology, soil, climate, vegetation cover, etc. (Carmean, 1975). In this study, the sampling sites were chosen to keep as many forest yield factors as constant as possible among the sites, except for parent matter mineralogy. In order to achieve this, all sites were located at about the same latitude (60880 – 62840 N), on podzolised glacial tills of felsic origin, and above the highest post-glacial sea level. Additional criteria for the sites were that they should have a minimum soil depth of 0.7 m, a ground slope not exceeding 5%, and a ground water level deeper than 1 m depth. The mean annual precipitation for the sites was 600–700 mm (Alexandersson and Andersson, 1995), and the annual mean temperature was 3 8C. Each site had well managed and fully closed forest stands aged 50–130 years (Table 1) that had not been thinned over the last 5 years (Eriksson, 1997). The mean age was slightly higher in the Dalarna area (118 years) than in the Ha¨ lsingland area (91 years) and the site index significantly higher ðF-probability ¼ 0:002Þ

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in Ha¨ lsingland. There was also a difference in tree species mixture between the two regions (Table 1). 2.2. Geology The mineralogy of the two areas is given in a broad outline in Table 2. The bedrock is quite different in the two investigated areas. In the Ha¨ lsingland area, there are early orogenic granitoids that blend with sedimentary rocks as quartzites, metaarkoses (Naggen) and metagreywackes towards the north. These are more or less gneissose and migmatized. Post-Jotnian dolerites are also a significant element of the bedrock in this region covering areas of several square km (Lundqvist et al., 1990). In the Dalarna area, a large part of the bedrock is made up of sandstone that in some places is covered by vast basalt beds. The basalt often makes up the hills and may be more than 100 m thick in some places. Porphyry and porphyrite is found in the eastern part, making up 20–30% of the total area (Hjelmqvist, 1966). In addition to the minerals given in the table,

Table 1 Descriptive statistics for soil mineralogy in the C horizon and stand data Variable

All data ðn ¼ 40Þ Mean

Mineralogy (wt.%) Quartz Plagioclase K-feldspar Biotite Muscovite Chlorite Vermiculite Amphibole Epidote Titanite Rutile Apatite Fe2O3 Al2O3 Stand data H100 (m) BAIa (m2 ha1 yr1) Age (years) Pine (%) Spruce (%) Others Altitude (m a.s.l.) Latitude a

Basal area increment.

52.89 16.58 13.30 1.04 7.32 2.15 – 2.23 2.45 0.37 0.34 0.27 0.71 0.63

S.D.

Ha¨ lsingland ðn ¼ 20Þ

Dalarna ðn ¼ 20Þ

Mean

Mean

S.D.

S.D.

15.38 8.40 4.14 0.56 2.43 1.62 – 2.10 1.44 0.18 0.11 0.10 0.51 0.48

39.41 24.20 15.23 1.39 7.06 2.96 1.39 3.93 3.10 0.43 0.37 0.31 0.92 0.42

3.31 2.79 3.80 0.53 2.32 1.41 1.20 1.50 1.23 0.10 0.10 0.09 0.48 0.30

66.37 8.96 11.38 0.71 7.57 1.33 – 0.52 1.80 0.31 0.32 0.23 0.52 0.83

9.58 3.85 3.60 0.34 2.58 1.41 – 0.83 1.35 0.23 0.11 0.08 0.46 0.55

21.3 0.33 104.2 69 28

2.8 0.17 29.2 39 36

22.6 0.42 90.9 51 45

2.4 0.18 27.0 42 39

20.0 0.24 117.5 88 12

2.6 0.10 25.3 25 25

339 618510

63.0 08640

334 628120

59.4 08200

344 608900

67.6 08080

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Table 2 The mineralogical composition of the bedrock in the investigated area (Hjelmqvist, 1966; Sna¨ ll, 1979; Lundqvist et al., 1990) (predominant minerals are annotated in the table)

Quartzite Sandstone Arkose Greywacke Granite Granodiorite Porphyry Porphyrite Dolerite Basalt

Quartz (vol.%)

K-feldspar/plagioclase (vol.%)

>80 60–80 60–75 20–50 20–40 20–40 20–40 20

<20K-feldsp. <40K-feldsp. 10–20 20–50 50–70b 50–70b 55–65 40–50plag. >50plag. >50plag.

Amphibole/pyroxene/chlorite (vol.%)a

Mica/chlorite (vol.%)

<5 <10 <10 <10 <20 <50c <50c

<5muscovite 10–20 20–60 5–20biotite 5–20biotite <5biotite <10biotite <5biotite <5biotite

a

Chloritized amphibole and pyroxene. Plagioclase predominant in granodiorite. c Including olivine. b

minor amounts of, e.g., epidote and titanite, are present. The tills in the area were formed by glacial comminution of the rocks and drift, and thus the mineralogical composition of the soil is strongly influenced by the regional bedrock composition (April and Newton, 1995). In central Sweden, it was found that 75–85% of till material <20 mm was made up of local bedrock (Linde´ n, 1975), although finer fractions usually have been transported longer distances (Perttunen, 1977) and may be influenced by distant rock types in the direction of ice movement (Haldorsen, 1977). Direction of ice movement is from NW to SE (Lundqvist, 1963, 1987) in this part of Sweden and in Dalarna also from N to S. The predominating soil types (FAO, 1988) in the study areas are Podzol on slopes and crests and Histosols or Gleysols where drainage is poor. All of the selected sites were characterised as Haplic Podzols. 2.3. Forest growth indicator The site index was estimated from the stand properties and was used as an indicator of potential forest growth. The current heights and ages of the dominant trees were translated to the expected height at an age of 100 years (H100) by means of height development curves (Ha¨ gglund, 1973, 1974). The site index was estimated for both Norway spruce (Picea abies (L.) Karst.) and Scots pine (Pinus sylvestris L.) for each site, but in the analysis the larger of the two values was taken as the general value of potential forest growth.

2.4. Soil sampling For each site, soil samples were taken at five points, 20 m apart and distributed in the shape of a cross, and subsequently bulked to form one composite sample. At each point, soil samples were taken from the O, A, E, upper B (B1), and the lower B horizon (B2). The upper B horizon was defined as the uppermost 5 cm of the B horizon and the lower B horizon as the remaining B horizon down to 40 cm. In the midpoint of each site, the C horizon was sampled at 0.7 m. 2.5. Soil analysis 2.5.1. Sample preparations and separations Before analysing soil chemical and physical properties the soil samples were dried at 20 8C. Roots were removed and grouped according to size in the fractions: 2 and >2 mm. The samples from the organic horizon were ground to <1 mm particle size. The matrix (<2 mm fraction) of the samples from the C horizon was separated into two grain size fractions (<63 and 63–2000 mm) for the chemical and mineralogical analyses. The separation was carried out by wet sieving through a 63 mm sieve using distilled water. The samples were then dried at 80 8C. A sub-sample of 15 g was splitted from each fraction and ground. This powder was used for the analysis of total elemental composition and extraction by aqua regia. The mineralogical composition of the two fractions was determined qualitatively by X-ray diffraction

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analysis (below). For the major minerals this analysis was carried out directly, whereas for the heavy minerals, the analysis was preceded by an enrichment of the heavy mineral fraction in order improve the accuracy in the determination. The samples were ground for 10 min in alcohol using a McCrone Micronising Mill. Before the separation, clay particles (<2 mm) were removed from the 0 to 63 mm fraction to facilitate dispersion of the particles in the liquid. The clay particles were separated from coarser particles by repeated sedimentation in a sedimentation cylinder. The 60–2000 mm fraction was crushed in an Ellis mortar and passed through a 0.5 mm sieve to reduce the number of mineral aggregates before separation in the heavy liquid. The heavy minerals were separated in a heavy liquid ðd ¼ 2:68 g cm3 Þ consisting of tetrabromethane ðd ¼ 2:96 g cm3 Þ and dimethylformamide ðd ¼ 0:95 g cm3 Þ. After separation, the heavy mineral fraction was ground and analysed in the same way as the major minerals. 2.5.2. X-ray diffraction analyses A Siemens D5000 (y–y) diffractometer with DIFFRAC-AT software was used for the analyses. The radiation (Ni-filtered, Cu Ka) was generated at 40 kV and 40 mA. The powdered samples were analysed using variable slits and a rotating sample holder (60 rpm). Scans were run from 2 to 658 (2y) with step size 0.028 (2y) and counting time 1 s step1 (scan rate 1.28 (2y) per minute). For oriented specimen analyses (clay fractions) scans were run from 2 to 358 (2y) with a fixed divergence slit (18) and a fixed sample holder. The minerals were identified from the X-ray diffraction (XRD) patterns by means of PDF computer database (PDF, 1994). 2.5.3. Soil chemistry and texture The samples of the C horizon (<2 mm) were analysed for both total elemental composition (TOT) and aqua regia extractable amounts (AQ). A sub-sample of 15 g was splitted from each fraction (<63 and 63– 2000 mm) and ground. For the analysis of TOT, the samples were fused with LiBO2 at 1000 8C and dissolved in 5% HNO3, and for AQ, samples of 1 g were leached in 15 ml hot acid (water bath 100 8C) for 1 h followed by leaching at room temperature for 20 h. The leachate was filtered, diluted and analysed further. In TOT and AQ analyses, the elemental composition in

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the solution was determined using plasma emission spectrometry, ICP-AES. The soil samples from the O, A, E, B1, and B2 horizons were analysed for chemical properties and pH. Total amounts of C, N, and S were analysed by dry combustion using a Leco CNS 1000 element analyser. Exchangeable cations were extracted with an NH4-Ac solution buffered at pH 7 and analysed by ICP-AES (Jobin Yvon JY 24). The pH was analysed both in distilled ðpHH2 O Þ water and in a 0.1 M KCl solution (pHKCl). Samples of 5 g mineral soil or 2 g organic matter were suspended in 25 ml solution and shaken for 16 h before analysis. The soil texture in the C horizon was analysed by sieving the fraction <2 mm and subsequently by sedigraph analysis (Micrometrics 5100) of the fraction <63 mm. Before analysis the soil samples were treated with dithionic citrate to remove sesquioxides. The stone content (>20 mm) was measured volumetrically in the field by repetitive sieving and weighing (cf. Eriksson and Holmgren, 1996). 2.6. Statistical analysis A PCA analysis (Webster and Oliver, 1990) was carried out on the mineralogy in order to investigate the relationships between the individual minerals and between the sampling sites, i.e. if the sampling sites were clustered in any way. This was done for all the sampling sites together and, after splitting the dataset, for the two regions separately. Since the absolute content of the various minerals differed widely, the analysis was carried out on the correlation matrix. The correlation between individual minerals and forest site index was investigated using the product moment correlation coefficient (Webster and Oliver, 1990) and regression analysis was used to establish relationships between forest site index and the soil mineralogy, soil texture, and the properties measured in the O, A, E, B1, and B2 horizons. This was done using multiple stepwise regressions. The criteria for adding a variable into the model was: fðRSS1  RSS0 Þ= ðd:f:1  d:f:0 Þg=fRSS0 =d:f:0 g < 1, and for removing a variable: fðRSS0  RSS1 Þ=ðd:f:0  d:f:1 Þg=fRSS1 = d:f:1 g > 1, where RSS denotes the residual sum of squares, d.f. the degrees of freedom, 0 the values of the current model, and 1 the values of the new model (Genstat 5 Committee, 1997). Adjusted r2 (Adj-r2)

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values were presented in order to obtain comparative r2-values although, the number of explanatory variables varied for the models. The regressions were made on standardised variables and the regression coefficients are thus b-coefficients. 2.7. Quantitative mineralogical assessment The normative mineralogical assessment (Sna¨ ll and Ek, 2000) was based on the chemical analyses of the soil samples in the C horizon (TOT and AQ), the XRD analyses, and data of the known chemical composition of minerals (based on values from literature or determined by microprobe analyses, Table 3). The calculations are described below. 2.7.1. Apatite, titanite, rutile and biotite The content of apatite was calculated from the P2O5 content (TOT) by dividing with the P2O5 content of apatite (Table 3). From the aqua regia extraction

analysis of titanium, the titanite (sphene) content was calculated by dividing extracted titanium by the assumed titanium content of titanite (cf. Table 3, all elements recalculated to oxides). Any undissolved titanium was considered as rutile. The biotite content was calculated by dividing aqua regia extracted K with that in the reference biotite (Table 3). 2.7.2. Plagioclase and K-feldspar Plagioclase is made up of albite and anorthite. The albite content was calculated from the Na2O content (TOT) by dividing by the Na2O content of albite (Table 3). This was later corrected, since a small part of the Na2O is bound to amphibole. If the type of plagioclase is known (albite, oligoclase, andesine, etc.) this will give the proportion between albite and anorthite. The choice of plagioclase used in the assessment was based on the bedrock descriptions of the area, XRD patterns, and microprobe analyses (Sna¨ ll and Ek, 2000) to determine the anorthite content of

Table 3 Chemical composition (wt.% oxides) of the minerals adopted for calculation of normative mineralogy of the samples Minerala

SiO2

Al2O3

Quartz Albite Anorthite K-feldspar Biotite I Biotite II Biotite III Muscovite I Muscovite IIb Chlorite I Chlorite IIc Chlorite III Vermiculite I Vermiculite II Pyroxene I Pyroxene II Amphibole I Amphibole II Epidote Garnet Titanite Rutile Apatite

100 68.7 43.2 64.8 34.7 35.6 34.8 47.9 46.7 25.0 29.7 36.9 34.0 32.0 50.6 54.7 43.0 50.4 38.0 36.9 31.0

19.4 36.6 18.3 17.8 19.5 15.1 34.1 23.8 19 14.9 18.1 16 10 2.2 3.0 13 7.1 25 22.1

a

Fe2O3

MgO

CaO

Na2O

K2O

TiO2

P2O5

11.8 20.2 24.3 21.4 32.5 1.7 10.2 30 32.9 20.1 28 20 10 7.0 17 8.2 12 38.4

7.4 9.3 4.5 1 2.5 13 14.75 10.8 8 18 15.2 34.0 10 18.1 4.9

0.1 0.3 1

16.9 9.7 8.6 9 8.8 9.5

3.3

0.8

21.5 0.93 11 12.1 23 1 28 55

0.07 2 0.8

0.01 1 0.6

41 100 42

In the mineralogical assessment the choice of minerals started with the mineral denoted I, and if the solutions to the calculations did not fulfil the aim of the probability controls, minerals denoted II or III were applied. b From Morad and AlDahan (1982). c ¨ je basalt (Sna¨ ll and Ek, 2000). Chemical composition obtained from microprobe analysis of chlorite in O

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the plagioclase. Knowing the type of plagioclase, the content of anorthite could be calculated. In the samples from the Ha¨ lsingland area, oligoclase was used in the calculations of the plagioclase content, while for the Dalarna area the Ca-poorer albite was used. The K-feldspar content of the samples was calculated from the K2O (TOT) content minus the K2O bound in biotite and amphibole (see calculation below). When muscovite was present in the XRD analysis of the samples, an alternative approach was taken. In this case, the K-feldspar content was determined from the peak height ratio between Kfeldspar and plagioclase in the XRD pattern and by using the previously determined plagioclase content as an internal standard in the relation (Brindley, 1980): wK-feldspar IK-feldspar ¼k wstandard Istandard where w is the weight of the mineral, I the peak height, and k a regression constant. This regression function was determined from known mixtures of various weight proportions of K-feldspar and plagioclase (unpublished experiments). 2.7.3. Calcite, pyroxene, amphibole, epidote and garnet After reduction of the total CaO content (TOT) for that allocated to apatite, titanite, and anorthite, the remaining CaO (CaOrem) was distributed between calcite, pyroxene, amphibole, epidote and garnet. To do this, the weight proportion between them was determined from the XRD pattern of the heavy fraction ðd > 2:68 g cm3 Þ of the samples. Peak intensity factors were determined from XRD scans of pure standards of these minerals, and by dividing peak intensity of each mineral in the sample with these factors the relative proportion of the minerals was obtained. Knowing these proportions (pcalcite, ppyroxene, pamphibole, pepidote and pgarnet), the average CaO content of this mixture (CaOmix) was calculated. The total sum of calcite, pyroxene, amphibole, epidote, and garnet contents was obtained from: 100

CaOrem =CaOmix . The obtained percentage was then distributed according to the proportions obtained earlier. In the analysed samples no calcite or pyroxene were found, and due to many missing values garnet was excluded from the further statistical analyses.

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2.7.4. Chlorite and vermiculite Aqua regia extracted Mg content was used for the calculation of chlorite þ vermiculite content in the samples, as both minerals were considered to be completely dissolved in the acid. First, the extracted Mg content was reduced for Mg allocated to biotite and Mg extracted from pyroxene and amphibole. The proportions of extracted chlorite and vermiculite were determined from the XRD patterns following the same principles as applied for pyroxene, amphibole, epidote, etc. (cf. above). Chlorite content obtained in this way was controlled against the chlorite content calculated from total MgO content after reduction for MgO allocated to biotite, muscovite, pyroxene and amphibole. If the discrepancy between the two calculations was too large (>5% relatively), the calculations were redone using other proportions between the Caand Mg-containing minerals, or by changing to another mineral (I, II, III, see Table 3). Particularly chlorite and vermiculite may have variable chemical composition in the soils, but also the micas. By iteration, the calculations were done until the discrepancy was below 5%. 2.7.5. Fe and Al oxides, quartz The amount of extractable ‘‘free’’ iron oxides was calculated as follows. From the total extractable Fe content, the amount extracted from pyroxene, amphibole, epidote, garnet and total iron of biotite, chlorite and vermiculite was subtracted. If the calculations returned a negative value one of the iron-containing minerals was replaced (e.g., from Chlorite I to Chlorite II, Table 3) until a positive value was obtained. Excess Al2O3 was obtained by subtracting Al2O3 bound in the accumulated mineral assemblage from the total Al2O3 content of the sample. Quartz content was obtained as the excess SiO2 after reduction of the SiO2 (TOT) for SiO2 assigned to all the other silicate minerals in the samples.

3. Results 3.1. Regional soil mineralogy The PCA score plot (Fig. 2) indicated that the data could be divided into two distinct sub-populations based on the mineralogy, since the sampling sites

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area (H) to a certain extent, while the Dalarna sites (D) were less separated along this axis. This implied different mineralogy within the two regions. The PCA loadings (Table 4) showed that quartz was the mineral having the largest influence on the first principal component (PC), and so the two regions mainly differed in quartz content, while the second PC was more difficult to interpret, with strong positive loading for K-feldspar and strong negative loading for muscovite. The first two axes accounted for 66.5% of the total variation (Table 5) and it could be concluded that a substantial within-region variation in mineralogy remained. The PCAs made for each region individually implied major differences in mineralogical composition within the two areas. In the Dalarna area, the first PC mainly accounted for variations in quartz content, while the second axis showed a gradient from the nutrient-poor minerals K-feldspar and mica, to mafic minerals, such as epidote, chlorite and biotite (Table 4). In the Ha¨ lsingland area, the first PC represented a

Fig. 2. Principal component score plot for soil mineralogy (H: sites in the Ha¨ lsingland area, D: sites in the Dalarna area).

formed two clusters corresponding to the geographical regions. This difference was along the diagonal between the first and second axis (Fig. 2). The second axis separated the sampling points of the Ha¨ lsingland Table 4 Summary of principal component analysis PC No.

1 2 3 4

Ha¨ lsingland

All data

Dalarna

Latent root

(%cum)

Latent root

(%cum)

Latent root

(%cum)

6.2 2.5 1.8 1.1

47.4 66.5 80.1 88.7

7.0 2.4 1.4 0.9

53.6 72.1 82.7 89.6

6.0 4.4 1.4 0.5

46.0 79.6 90.1 94.1

Table 5 PCA loadings for 1st and 2nd principal component, for all sites and for the two regions separately Mineral

Quartz Plagioclase K-feldspar Biotite Muscovite Chlorite Amphibole Epidote Titanite Rutile Apatite Fe2O3 Al2O3

Ha¨ lsingland

All data

Dalarna

PC 1

PC 2

PC 1

PC 2

PC 1

PC 2

0.34 0.29 0.05 0.29 0.09 0.35 0.34 0.34 0.32 0.29 0.34 0.21 0.04

0.26 0.39 0.50 0.00 0.45 0.11 0.16 0.08 0.19 0.35 0.17 0.17 0.27

0.12 0.25 0.33 0.32 0.30 0.30 0.25 0.34 0.32 0.34 0.30 0.19 0.08

0.54 0.38 0.20 0.09 0.12 0.32 0.38 0.06 0.18 0.14 0.32 0.30 0.05

0.30 0.23 0.04 0.09 0.16 0.34 0.25 0.31 0.36 0.33 0.37 0.36 0.20

0.30 0.29 0.40 0.44 0.28 0.16 0.32 0.24 0.21 0.11 0.01 0.12 0.36

J. Stendahl et al. / Forest Ecology and Management 170 (2002) 75–88

gradient from K-feldspar and plagioclase to mafic minerals and mica (Table 4). The mineralogical composition in the two regions had a similar pattern. Both were characterised by a large variation in the content of one or two abundant minerals (in Dalarna quartz and in Ha¨ lsingland K-feldspar and plagioclase), while the distribution between the other minerals was less variable. 3.2. Relationships between soil mineralogy and forest site index 3.2.1. Correlation coefficients For the whole dataset, epidote and amphibole were the minerals most positively related to site index (r ¼ 0:55 and 0.56, Table 6) together with plagioclase ðr ¼ 0:48Þ, while quartz was the most negatively related ðr ¼ 0:48Þ. After splitting the data according to the two regions the correlations differed between the two regions for several of the minerals (Table 6). In the Dalarna area chlorite ðr ¼ 0:54Þ, epidote ðr ¼ 0:47Þ and amphibole ðr ¼ 0:35Þ were the minerals that correlated strongest to the forest site index, while in the Ha¨ lsingland area, K-feldspar ðr ¼ 0:56Þ, epidote ðr ¼ 0:33Þ, amphibole ðr ¼ 0:33Þ and quartz ðr ¼ 0:33Þ were the most strongly correlated. In the Ha¨ lsingland area, it is interesting to note that the site index was independent of chlorite. Some minerals were oppositely correlated to forest site index in the two Table 6 Correlation between site index (H100) and soil mineralogy in the C horizon Mineral

Quartz Plagioclase K-feldspar Biotite Muscovite Chlorite Vermiculite Amphibole Epidote Titanite Rutile Apatite Fe2O3 Al2O3

H100 All data

Ha¨ lsingland

Dalarna

0.48 0.48 0.01 0.37 0.03 0.44 0.11 0.56 0.55 0.43 0.27 0.30 0.29 0.13

0.33 0.21 0.56 0.19 0.13 0.09 0.15 0.33 0.33 0.29 0.28 0.00 0.01 0.17

0.28 0.31 0.03 0.00 0.08 0.54 0 0.35 0.47 0.39 0.11 0.21 0.22 0.06

83

geological regions. The biotite, e.g., was positively correlated with forest site index in the Ha¨ lsingland area, while in the Dalarna area biotite was uncorrelated to site index. Plagioclase is similarly positively correlated with forest site index in the Dalarna sites and negatively correlated in the Ha¨ lsingland sites. 3.2.2. Regression functions The stepwise regressions for all sites with the dummy variable for region (Table 7) included several contrasts between soil minerals and region in the final function. This shows that the relationships between site index and soil mineralogy were significantly different between the two regions. A large positive (þ) coefficient was found for the contrast between plagioclase and region and a large negative coefficient () for the contrast between K-feldspar and region (Table 7). Epidote and amphibole were the only minerals that were consistent in their relation to site index for the two regions. The regression explained about half of the variation in site index ðr 2 ¼ 51:9%Þ. Separate regression functions were then made for each region. In the Ha¨ lsingland area plagioclase (þ), Kfeldspar () and chlorite () had the largest coefficients in the regression function (Table 8). In the Dalarna area, chlorite had a large coefficient and notably amphibole, which is easily weathered, had a negative coefficient (Table 8). The function for the Ha¨ lsingland area explained more of the variation in site index ðr 2 ¼ 60:8%Þ than that of the Dalarna area ðr 2 ¼ 36:6%Þ. The influence of texture on forest site index was fairly strong in the Ha¨ lsingland area (r 2 ¼ 48:3%, coefficients not presented), whereas it was non-existent in the Dalarna area ðr 2 ¼ 0:8%Þ.

Table 7 Regression functions between forest site index (H100) and soil mineralogy for all sites, including the dummy variable ‘‘Reg’’ for region (Ha¨ lsingland: 1, Dalarna: 0) Variable

Estimate

S.E.

t-probability

Plagioclase Reg Epidote Amphibole Chlorite Reg K-feldspar Reg r2 ¼ 51:9***

5.31 1.74 0.92 1.92 4.40

1.43 0.566 0.739 0.825 1.25

<0.001 0.004 0.223 0.027 0.001

***

Significant at the 0.001 F-probability level.

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Table 8 Regression functions between soil mineralogy in the C horizon and forest site index (H100) for the two regions Dalarna and Ha¨ lsingland Ha¨ lsingland

Dalarna

Variable

Estimate

S.E.

t-probability

Variable

Estimate

S.E.

t-probability

Plagioclase Epidote Al2O3 K-feldspar Chlorite r 2 ¼ 60:8**

5.33 2.19 0.82 2.75 2.92

1.85 0.99 0.64 0.76 0.76

0.014 0.048 0.224 0.003 0.002

Chlorite Fe2O3 Rutile Amphibolelog

3.26 1.44 1.95 2.02

0.90 1.29 1.02 0.91

0.002 0.28 0.074 0.043

*

r2 ¼ 36:6*

Significant at the 0.05 F-probability level. Significant at the 0.01 F-probability level.

**

Table 9 Regression functions between soil chemistry in the O–B2 horizon and forest site index (H100) for the two regions Dalarna and Ha¨ lsingland Ha¨ lsingland

Dalarna

Variable

Estimate

S.E.

t-probability

Variable

O horizon N S Mg CEC Ca Mn H r2 ¼ 80:1***

2.52 0.66 0.36 0.71 1.73 2.33 4.21

0.58 0.30 0.29 0.69 0.88 0.50 0.99

0.001 0.046 0.233 0.321 0.074 <0.001 0.001

N Mn pHH2 O S H

A horizon C Ca Mg Na H K N r2 ¼ 78:8***

3.55 2.94 0.83 0.44 1.70 1.83 2.29

E horizon Ca N pHKCl CEC

S.E.

t-probability

4.88 4.66 3.97 1.04 0.87

1.52 2.08 2.08 0.76 0.59

0.007 0.043 0.078 0.197 0.164

1.64

0.72

0.034

3.21 2.86 2.72 2.20 2.52

0.71 0.59 0.55 0.83 0.81

<0.001 <0.001 <0.001 0.021 0.009

1.56 1.92

0.76 0.72

0.057 0.017

r2 ¼ 59:2** 1.19 0.57 0.45 0.34 0.61 0.40 0.78

0.014 <0.001 0.096 0.227 0.019 <0.001 0.015

N

r2 ¼ 18:3* 2.29 0.61 0.66 2.94

0.60 0.59 0.45 1.12

0.002 0.326 0.17 0.022

1.82 1.32 0.66 0.56 0.92 1.01

0.31 0.41 0.41 0.33 0.34 0.68

<0.001 0.007 0.137 0.113 0.019 0.166

r2 ¼ 52:9*** B1 horizon Ca C Na Mn K CEC r2 ¼ 78:1***

Estimate

pHH2 O H Na Ca N r2 ¼ 68:3*** Mn N

r2 ¼ 39:4**

J. Stendahl et al. / Forest Ecology and Management 170 (2002) 75–88

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Table 9 (Continued ) Ha¨ lsingland

Dalarna

Variable

Estimate

S.E.

t-probability

Variable

Estimate

S.E.

t-probability

B2 horizon Mg C N K CEC r2 ¼ 65:5***

1.94 1.47 1.23 1.10 1.61

0.52 0.55 0.39 0.42 0.53

0.002 0.019 0.007 0.019 0.009

CEC pHH2 O Na

2.30 1.53 1.64

0.76 0.85 1.28

0.008 0.09 0.219

r 2 ¼ 41:0*

*

Significant at the 0.05 F-probability level. Significant at the 0.01 F-probability level. *** Significant at the 0.001 F-probability level. **

3.3. Relationships between topsoil properties and forest site index In the O horizon of the Ha¨ lsingland area, nitrogen was positively related (þ) to site index (Table 9) and hydrogen the most negatively related (). For the A and B horizons, the functions were characterised by large coefficients for the base cations, mainly for calcium (þ). There was a tendency that the functional relations between soil chemistry in the O–B horizon and site index had smaller r2-value the deeper the horizon (Table 9). In the Dalarna area, nitrogen had the largest coefficient (þ) for most soil horizons (O, A and B1), and for the O and B2 horizons, pH (þ) was influential as well. For the E horizon, calcium () and hydrogen (þ) had the largest coefficients in the function. Generally, the functional relations between the soil properties and site index were much stronger in the Ha¨ lsingland area than the Dalarna area (Table 9), one exception being the data from the E horizon.

4. Discussion 4.1. Mineralogical influence on site index In the Ha¨ lsingland area, where the mineralogy was rich in mafic minerals, there was an evident positive correlation between the contents of the major easily weatherable heavy minerals, viz. amphibole, epidote and biotite, and forest site index (cf. Table 6). This is in agreement with the general idea about mineralogical quality of the soils and forest growth (Tamm, 1934;

Eklund, 1953). However, the easily weatherable mineral chlorite was weakly negatively correlated. Most likely the supply of Mg is covered well by the weathering of amphibole, which in addition to Mg contains Ca as well as K (cf. Table 3). A balance of easily available nutrients favours forest growth and the surplus of one nutrient, in this case Mg from chlorite, will have no effect (Leibig’s rule of minimum). Any additional need for Mg and K is supplied by the weathering of biotite present in the soils. The strong positive correlation between chlorite and site index in the Dalarna area is explained by the deficit of other easily weatherable Mg-containing minerals. The other base cations are supplied from epidote (Ca) and the less weatherable feldspars (Ca from plagioclase and K from K-feldspar). In the Ha¨ lsingland area, there was also a negative correlation between the feldspars and site index, especially for K-feldspar. This indicates that low site index in this region is associated with granite bedrocks that are comparatively poor in mafic minerals. Large granite areas are found in the central and southern parts of the Ha¨ lsingland area, while in the northern part of the region there is a large area of dolerite bedrock with a very high content of mafic minerals (Table 2). The unexpected positive correlation between quartz content and site index in Ha¨ lsingland can be explained by the geology of the area. In the northern part of the region, there are quartzites and arkoses rich in quartz that co-occur with dolerites rich in mafic minerals. When the continental ice sheet moved across the area it formed tills out of a mixture of the different rock types and so the soils of the area are rich in mafic minerals as well as in quartz. Such a

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soil will be more favourable for the site conditions than the soils overlaying the feldspar-rich granite in other parts of the region, which is poor in mafic minerals. In the poor Dalarna area, chlorite and epidote are the major easily weatherable minerals in the tills and they are also strongly correlated with the site index (Table 6). The pyroxene and amphibole once present in the rock were more or less chloritised by retrograde metamorphism affecting the region in post-Svecokarelian time (Nystro¨ m and Levi, 1980) and any remaining amphibole may be found as small inclusions within the very fine-grained porphyry aggregates in the soil. As inclusions, this amphibole is not as exposed to the soil solution, which may be the explanation of the negative correlation for this easily weatherable mineral and site index in this region. In the Dalarna area, no correlation between biotite and site index was obtained, although, biotite is an easily weathered mineral. This is probably due to an error in the determination of the biotite, which assumed that all aqua regia leachable K belonged to biotite. The aqua regia leaching was applied on ground samples, which increases leachability of K from other minerals, mainly K-feldspar (cf. Sna¨ ll and Liljefors, 2000), and thus the biotite content may have been overestimated. This can also be seen in the association between biotite and K-feldspar in the PCA analysis (Table 5, Dalarna). In the Dalarna area, where the biotite content was small, the relative contribution of K from K-feldspar will be greater than in the samples from the Ha¨ lsingland area. 4.2. Mineralogy and topsoil data vs. site index The r2-values were larger in the regression functions between mineralogy and the site index in the Ha¨ lsingland area than in Dalarna. One factor, which may give rise to this pattern, is the variability in soil mineralogy in the regions. Soil-site studies are best made under variable conditions (Carmean, 1975), and the sites in Ha¨ lsingland area were more variable than those in the Dalarna area. Another factor is differences in plant nutrition for different soil fertilities. In mature forest stands, the recycling of nutrients from perished tissues such as leaves and branches covers a large part of the nutrition, and nutrients accumulate in the forest stand and in the topsoil over the rotation period

(Fischer and Binkley, 2000). These factors will reduce the influence of soil properties on the productivity of the stand, an effect that may be even more pronounced on poor sites (Miller, 1995). As the vegetation is a factor influencing the weathering rate (Quideau et al., 1996), the differences between mineralogically poor and rich sites are further enhanced. The Dalarna area had poorer mineralogy compared to the Ha¨ lsingland area, which may reduce the influence of soil properties in this area. Stronger associations were generally found between topsoil properties and the site index (Table 9) than for mineralogy. This is an obvious result because of the interaction between the forest stand and the upper soil horizons by nutrient cycling. There was a pattern of decreasing association between soil properties and site index in the Ha¨ lsingland area, which nicely illustrates this effect. The r2-value for the O horizon was large (80.1%) and for the B2 horizon it was much smaller ðr 2 ¼ 65:5%Þ, which was close to that of the function based on mineralogy ðr 2 ¼ 60:8%Þ. In the Dalarna area, the function for the E horizon had a negative influence of calcium and positive hydrogen. This may indicate a relation between leaching, which depletes the E horizon of Ca, and site index. The functional relations were stronger in the Ha¨ lsingland, which may be explained by the soil–plant interactions—a more productive stand will result in a larger litterfall, nutrient uptake, and biological weathering, which will affect the upper part of the soil more than a less productive site. For example, Lundell (1989) showed that the amount of Ca and Mg adsorbed to resin bags in the topsoil was positively influenced by site index. On a less fertile soil one can expect a relatively larger influence of the abiotic soil-forming factors that are independent of the stand itself. This was not apparent in the geologically poor Dalarna area though, which had smaller influence of both mineralogy and texture on site index than the Ha¨ lsingland area.

5. Conclusions The general pattern in the correlation between minerals and site index was that easily weathered mafic minerals correlated positively to site index, and that a balance of the base cations is necessary

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for forest site quality. In the mineralogically rich region, no single mafic mineral was crucial for site index, while in the poor area chlorite was of great importance in the absence of other Mg-rich minerals. Epidote was consistently well correlated to site index in the two regions. For some minerals, the correlation was the opposite for the two regions. The functional relations based on soil properties explained more of site index in the mineralogically rich area, and the functions between mineralogy and site index could be improved by dividing the data according to the two geological regions.

Acknowledgements We wish to thank Carl Petter Eriksson for his great work in sampling and analysing much of the material for this study. This work was funded by the Geological Survey of Sweden and the Swedish Council of Forest and Agricultural Research.

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