Science of the Total Environment 521–522 (2015) 90–100

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Effect of variable soil texture, metal saturation of soil organic matter (SOM) and tree species composition on spatial distribution of SOM in forest soils in Poland Piotr Gruba a,⁎, Socha Jarosław b, Błońska Ewa a, Lasota Jarosław a a b

Department of Forest Soil Science, University of Agriculture, Al. 29 Listopada 46, Krakow 31-425, Poland Department of Biometry and Forest Productivity, University of Agriculture, Al. 29 Listopada 46, Krakow 31-425, Poland

H I G H L I G H T S • • • • •

We analysed the spatial distribution of SOC and its fractions in forest soils Soils with low FF (b 20 %) adsorbed C more effectively than FF-rich soils (> 20 %) The content of FF was found to enhance the content of C in MAF and fLF fractions The content of stable C (in oLF and MAF) was also influenced by metal saturation We found a positive effect of beech on the CfLF and fir on the CoLF content

a r t i c l e

i n f o

Article history: Received 9 January 2015 Received in revised form 9 March 2015 Accepted 9 March 2015 Available online xxxx Editor: F.M. Tack Keywords: Soil organic carbon stocks Density fractions Forest soils Beech Fir Organic layer Fine fraction content

a b s t r a c t In this study we investigated the effect of fine (ϕ b 0.05 mm) fraction, i.e., silt + clay (FF) content in soils, site moisture, metal (Al and Fe) of soil organic matter (SOM) and forest species composition on the spatial distribution of carbon (C) pools in forest soils at the landscape scale. We established 275 plots in regular 200 × 200 m grid in a forested area of 14.4 km2. Fieldwork included soil sampling of the organic horizon, mineral topsoil and subsoil down to 40 cm deep. We analysed the vertical and horizontal distribution of soil organic carbon (SOC) stocks, as well as the quantity of physically separated fractions including the free light (fLF), occluded light (oLF) and mineral associated fractions (MAF) in the mineral topsoil (A, AE) horizons. Distribution of C in soils was predominantly affected by the variation in the FF content. In soils richer in the FF more SOC was accumulated in mineral horizons and less in the organic horizons. Accumulation of SOC in mineral soil was also positively affected by the degree of saturation of SOM with Al and Fe. The increasing share of beech influenced the distribution of C stock in soil profiles by reducing the depth of O horizon and increasing C stored in mineral soil. The content of FF was positively correlated with the content of C in MAF and fLF fractions. The content of oLF and MAF fractions was also positively influenced by a higher degree of metal saturation, particularly Al. Our results confirmed that Al plays an important role in the stabilization of SOM inside aggregates (CoLF) and as in CMAF fractions. We also found a significant, positive effect of beech on the CfLF and fir on the CoLF content. © 2015 Elsevier B.V. All rights reserved.

1. Introduction In temperate forest ecosystems, soil organic carbon (SOC) is divided between the organic layer at the surface and mineral soil (e.g., de Vries et al., 2003; Schulp et al., 2008; Deluca and Boisvenue, 2012). SOC that has accumulated in the organic horizon (SOCO) is commonly assumed to decompose relatively quickly as a non-protected fraction of soil organic matter (SOM) that is vulnerable to forest management, e.g., a shift of forest species composition (Olsson et al., 1996; Jandl et al., ⁎ Corresponding author. E-mail address: [email protected] (P. Gruba).

http://dx.doi.org/10.1016/j.scitotenv.2015.03.100 0048-9697/© 2015 Elsevier B.V. All rights reserved.

2007; Galka et al., 2014). In contrast, SOC accumulated in mineral soil (SOCMin) is protected by various mechanisms making it more stable (Hassink, 1997). In fact, SOCMin exists in soils as labile and stabilized fractions, especially in the topsoil horizon (Chabbi et al., 2009; Grüneberg et al., 2013), while subsoil SOM is more stable (Rumpel, 2004; Mikutta et al., 2005; Chabbi et al., 2009). The low density of the labile fraction (b1.7 g cm− 3) gives it its name, the free light fraction (fLF); this fraction is mostly root-derived coarse plant fragments (e.g., Mambelli et al., 2011). Some of the LF of mineral soil may become stabilized by occlusion inside aggregates (so-called occluded light fraction (oLF)) (e.g., Sollins et al., 1996; von Lützow et al., 2007). The stabilized fraction of SOM is known as the mineral associated fraction (MAF).

P. Gruba et al. / Science of the Total Environment 521–522 (2015) 90–100

Organic matter is also adsorbed on the surface of soil mineral fine fractions (FF), i.e., clay and silt particles (e.g., Sollins et al., 2006; Grüneberg et al., 2013) or amorphous substances (Al and Fe hydroxides) (e.g., Kleber et al., 2005; Saidy et al., 2012). The observed spatial variation of forest SOC stocks at different spatial scales is related to many factors and can be generalised to geologically- and forest-related (e.g., Schulp et al., 2008; Paluch and Gruba, 2012). Properties of soils inherited from the parent material determine the presence of conservation agents, i.e., content of clay and silt fraction, amorphous Fe and Al hydroxides (Hassink, 1997; Kaiser et al., 1996; Kleber et al, 2005; Saidy et al., 2012) and metal saturation of SOM (Dijkstra and Fitzhugh, 2003; Hobbie et al., 2007; Heckman et al., 2009). The effects of tree species on SOC is usually seen in the way SOC is partitioned between organic horizon and mineral soil, rather than in differences in the total stock of SOC (e.g., Oostra et al., 2006; Schulp et al., 2008; Gurmesa et al., 2013). This suggests that some species may be better than others at stabilizing C in mineral soil (Vesterdal et al., 2013). Generally, a thick organic horizon is more likely to occur under coniferous forest. Moreover, when forest stands grow in similar site condition (e.g., common garden experiments), coniferous tree species accumulated more C as SOCO and tended to have less C as SOCMin. Nevertheless, only scattered information exists related to the effects of forest species composition on SOCMin, and only few studies have confirmed the significant influence of forest species (Vesterdal et al., 2013). The higher mass of the organic horizon typically found under coniferous species is often attributed to the greater recalcitrance of the litter (Schöning et al., 2005; Marschner et al., 2008; Mambelli et al., 2011). Species-specific biomass production and the architecture of the rooting system are also important in determining the vertical distribution of SOC (Helmisaari and Makkonen, 2002; Oostra et al., 2006; Jandl et al., 2007; Spielvogel et al., 2014). Current knowledge of the mechanism of chemical adsorption of SOM in soils implies that the quality of SOM derived from different species may have an influence on its rate of accumulation. SOM derived from different species has significantly different chemical composition, resulting in variations in the nitrogen content (Giardina and Ryan, 2001; Augusto et al., 2002). In particular, these nitrogenous compounds (amine, amide) may adhere directly to mineral surfaces (Schöning et al., 2005; Kleber et al., 2007). Peptidic compounds, along with ligand-exchanged carboxylic compounds, could then form a stable inner organic layer onto which other organics could be more readily adsorbed than onto mineral surfaces (Kleber et al., 2007). Additionally, tree species also influence acid strength (Binkley et al., 1989) and potential cation binding ability of SOM (Gruba and Mulder, 2015). Related to tree species soil acidity and complexation of metals (particularly Al and Fe) to SOM may affect the stability of SOM in soils via cation bridging between SOM and minerals (Mulder et al., 2001; Hobbie et al., 2007). Recently, Mueller et al. (2012) proposed a conceptual model of SOM stabilization in forest soil as an effect of tree species on soil acidification, dissolution of Al and Fe-containing minerals and metal complexation to SOM. Investigations of the effect of tree species on the quantity and quality of soil C are mostly based on a common garden approach where parent material remains in a relatively stable form (e.g., Hobbie et al., 2007; Mueller et al., 2012). However, in natural and near-natural natural forest ecosystems, the species composition of forest stands tends to follow certain gradients of climate, soil types, and soil water conditions. This type of multi-variable approach requires a large number of replications and appropriate statistical methods. Additionally, large numbers of sampling points, established in a particular area will allow the use of geostatistics; geostatistics provide powerful tools for the analysis and interpolation of spatially autocorrelated data (Webster, 1985; Goovaerts, 1999). The objective of this research was to investigate the importance of variable soil texture (from sands to loams), current site moisture, metal (Al, Fe) saturation of SOM and forest species composition for the spatial (vertical and horizontal) distribution of labile and stable

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pools of C in forest soils at the landscape scale. Particularly, we focused on the accumulation of C in the physically separated fractions: fLF, oLF and MAF of mineral topsoil horizons. These investigations were conducted in an area where local species composition reflects the demands for tree species (with domination of Silver fir and European beech) to the site conditions.

2. Materials and methods 2.1. Study site The study site, located on the Mesozoic shield in the Świętokrzyskie Mountains of central Poland, covers 14.4 km2 in the central part of a fir forest managed by the State Forest Administration (Fig. 1). The ominant tree species were silver fir (Abies alba Mill.) and European beech (Fagus sylvatica L.), with a mixture of Scots pine (Pinus sylvestris L.), European oak (Quercus robur L.) and common birch (Betula verrucosa Ehrh.). No site preparation had previously disturbed the soils in the study area because this forest had grown from natural regeneration. The terrain at the study site was gently inclined to the NW, and the elevation varied from 290 to 412 m above sea level (a.s.l.). According to the Detailed Geological Map of Poland (1:50,000, sheets 778 and 779, Polish Geological Institute, Krajewski, 1955), the local soils are derived from three kinds of parent material (Fig. 1): 1) Triassic sandstones (TS) and claystones (TC). Layers of sandstones were formed in the lower Triassic. They are red in colour (the so-called Buntsandstein sandstone) as a result of an admixture of hematite. Kaolinite is a major clay mineral in sandstones and claystones. TS and TC occur at the elevations of ca. 350–412 m a.s.l. 2) Quaternary sands (QSW), derived from the material washed from weathering of sandstones, underlain by sandstones and claystones. 3) Quaternary sands of fluvio-glacial origin (QSFG); they occur at elevations below 325 m a.s.l. The soils were classified as Dystric Cambisols, usually developed from TS and TC, Haplic Luvisols (Abruptic) derived from QWS, and Albic Podzols developed on QSFG (WRB, 2006).

2.2. Sampling scheme Originally, sampling was planned to cover an area of 3.8 × 3.8 km, in a regular 200 m × 200 m grid. For various reasons (mostly limited accessibility), we established 275 plots (see Fig. 1). Each plot was represented by one shallow soil profile located at the plot's centre. The organic horizons (O) were sampled using a 20 × 20 cm metal frame. We also measured its thickness. Next, the border between the O and mineral soil was considered to be a depth of 0. The mineral topsoil (A or E) horizon was measured from the 0 depth and sampled. The subsoil horizon (mostly B) was sampled from the bottom of topsoil horizon to 40 cm deep. During the sampling we estimated the percentage volume of rock fragments, if present.

2.3. Forest measurement Field data were collected from all 275 sample plots that varied from 0.02 to 0.10 ha depending on number of trees. Diameter at breast height (dbh), and tree heights (h) was measured for trees in the sample plots with an outside bark dbh greater than 7 cm. Aboveground biomass (AGB) at the plot level was calculated as the sum of individual tree's biomass. The aboveground biomass of individual trees was calculated using species specific allometric equations with dbh and h as explanatory variables (Zianis and Mencuccini, 2004; Muukkonen and Mäkipää, 2006). Species composition on individual sample plots was calculated as the ratio of individual tree species biomass to total plot AGB.

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Fig. 1. a) Location of the investigation sites in Europe and Poland and b) the generalised geological map developed based on a detailed geological map of Poland. TS, Triassic sandstones; TC, Triassic claystones; QSW, Quaternary sands derived from the material washed from weathering sandstones; QSFG, Quaternary sands of fluvioglacial origin. Black squares indicate the investigation sites.

2.4. Site moisture Site moisture of each study plot was classified based on the site moisture index (SMI). The SMI was calculated based on ecological plant indicators (adopted from Ellenberg, 1979), and modified to the local conditions by Zarzycki et al. (2002). The SMI was classified as follows: 1) very dry, 2) dry, 3) fresh, 4) moist, 5) wet, and 6) aquatic.

2.5. Basic laboratory analysis Prior to soil sieving living roots were sorted out. Soil samples were air-dried for about one week at room temperature, and then sieved with a 2 mm sieve. The particle size composition was determined using laser diffraction (Analysette 22, Fritsch, Idar-Oberstein, Germany). pH was measured electrochemically with a combination electrode in a suspension with distilled water (1:5 mass-to-volume ratio) after 24 h of equilibration (Buurman et al., 1996). Exchangeable calcium, potassium, magnesium, and sodium were extracted with 1 mol L− 1 NH4Cl. Soil samples were mixed with an extractant (10 g in 30 mL) and equilibrated. After 24 h, the suspensions were filtered (0.45-μm Millipore), the soils were washed with additional extractant, and the total volume was made up to 100 mL (Jackson, 1958). The concentration of cations was determined by an ICP (ICP-OES Thermo iCAP 6500 DUO, Thermo Fisher Scientific, Cambridge, U.K.). Total acidity (TA) of the soil was measured after extracting 5 g of soil with 30 mL 1 mol L−1 (CH3COO)2Ca (shaking time 1 h), followed by filtration. Soil on the filter was washed several times by extractant solution up to volume 200 mL (Buurman et al., 1996). 25 mL of the obtained solution was titrated by potentiometric titration (automatic titrator, Mettler Toledo, Inc.) to pH 8.2 with 0.1 mol L−1 NaOH. Cation exchange capacity (CEC) was calculated as a sum of base cations (BC) and TA. Acid oxalate-extractable aluminium (Alox) and iron (Feox) were estimated by extractions with 0.2 M acid ammonium-oxalate solution at pH 3.0 for 4 h in the dark. All extraction procedures described above were followed by filtration through a 0.45-mm Millipore membrane filter. On selected 20 fine-ground subsamples, representing a transect through the investigating area, we performed analysis of mineral composition XRD (D2-Phaser, Bruker).

The subsamples were ground using a ball mill (Fritsch) to improve their homogeneity. We measured the content of soil total carbon (Ct) and nitrogen (Nt) in the fine subsamples with a LECO CNS True Mac Analyzer (Leco, St. Joseph, MI, USA). As all samples were carbonate-free, the Ct was assumed to be organic carbon (Ct = Corg). 2.6. Physical separation of soil organic matter fractions Physical separation of SOM fractions was performed on only 275 samples from the topsoil horizons (A and E). We employed a method designed for modelling the C content (Sohi et al., 2001). A 15 g sample of soil was placed in a 200 mL centrifuge tube and 90 mL of NaI (1.7 g cm−3) was added. Each tube was gently shaken for 1 min and centrifuged for 30 min (5000 g). The fLF was aspired and collected on a glass filter. The soil remaining on the bottom of the centrifuge tubes was mixed with another portion of 90 mL of NaI and subjected to sonication (60 W for 200 s) to destroy aggregates. After centrifugation, the matter released from aggregate oLF was aspired and collected on a glass filter. The remaining fraction was assumed to consist of MAFs of SOM. After drying (40 °C), these subsamples were weighted and analysed for CfLF, CoLF and CMAF, respectively, as well as the content of NfLF, NoLF and NMAF using an LECO CNS analyser. 2.7. Statistical and geostatistical analysis Due to the presence of roots and rock fragments the use of metal cores to measure bulk density (bD) of mineral soil was very difficult. Therefore we estimated bD of mineral soil on the basis of Ct content, using the equation developed by Alexander (1980): 0:5

bD ¼ 1:66–0:308ðCt Þ

ð1Þ

which was found to be most accurate for mineral horizons of Polish forest soils (Brożek et al., 2007). Multiple regressions were used to describe the relationship between SOC and independent variables such as different soil properties and stand characteristics. Due to strong right-skewness, heteroscedasticity or observed nonlinearity between analysed variables most of them

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(except for pH and C/N), were transformed. Strong relationships between the independent variables, termed ‘collinearity’, cause the least squares regression coefficients to become unstable: coefficient standard errors are large, reflecting the imprecision of parameter estimation. Consequently, the parameter confidence intervals are broad. The impact of collinearity on the precision of estimation was captured by the variance inflation factor (VIF) (Fox, 1991). VIF was calculated according to Eq. (2): VI F j ¼

1 1−R2j

ð2Þ

where R2j is the coefficient of determination between independent variable j and other independent variables. According to commonly accepted standards (Fox, 1991) it was assumed that VIF value should not exceed a value of 2. Homoscedasticity and the distribution of residual values against values predicted from the models were analysed graphically and tested using White's (1980) test. Multiple regressions, descriptive statistics and analysis of variance were performed using STATISTICA 10 software. Means are accompanied with (±) standard errors throughout the manuscript. The measured properties of soil samples taken from neighbouring points often tend to show more similarity than those separated by a larger distance. Therefore, the semivariance γ (h) increases as the distance between sampling points increases (Nielsen and Wendroth, 2003). A semivariogram was calculated based on Eq. (3).

1 X 2 ½Aðxi Þ−Aðxi þ hÞ 2NðhÞ i¼1 N ðhÞ

γ ðhÞ ¼

ð3Þ

1 X ½Aðxi Þ−Aðxi þ hÞ  ½Bðxi Þ−Bðxi þ hÞ 2NðhÞ i¼1 N ðhÞ

ð4Þ

where A(xi) and B(xi) are the values of the A and B variables at the (xi) location while A(xi + h) and B(xi + h) represent values of A and B at the xi + h location. All data were standardized to zero mean and unit variance prior to cross-variogram calculation. Cross-variograms were not used to assess the significance of the correlation; therefore, the hulls of perfect correlation (Wackernagel, 2003; Borůvka et al., 2007) are not shown. Instead, they were used to judge

Table 1 Texture of the mineral topsoil horizons (%). Fraction

N

Mean

Min.

Max.

Std. dev.

Sand Silt Clay FF

275

81 16 4 19

28 2 0 3

97 61 29 65

14 11 4 14

FF — fine fractions (silt + clay).

the general tendencies (e.g., positive/negative, stronger/weaker correlation) in the context of the spatial dependence. Geostatistical analysis and mapping were performed with the use of GS 9+ (Gamma Design) and Surfer 11 (Golden Software). 3. Results 3.1. Mineralogy and soil texture

where N(h) is the number of pairs of data points separated by the lag distance h. A(xi) and A(xi + h) represent the values of A at the xi and xi + h locations. The semivariograms were then fitted with models used for interpolation by kriging. Fig. 3a describes examples of parameters of variograms. The semivariance increase with increasing distance, then levels off. The distance [m] at which the plateau is achieved is called the ‘range,’ and the semivariance value of the plateau is the ‘sill’ (C0 + C). The modelled semivariance at the zero distance is the nugget variance (C0) (see example in Fig. 3a). To examine the spatial relationships between different types of variables, the experimental cross-variogram was defined by an extension of the calculation of ordinary semivariograms (Nielsen and Wendroth, 2003, Eq. (4)): Γ ðhÞ ¼

Fig. 2. Map of the fine fractions (FF) content [%] in the topsoil horizons.

Soils derived from the different types of parent material, namely Triassic sandstones (TS) and claystones (TC), and Quarternary sands (QSW and QSFG) had similar mineral composition. Results of the XRD analysis of the selected 20 powder samples, representing a SE–NW transect across the investigated area, demonstrated a domination of quartz with minor (from traces to 12%) admixture of kaolinite. Kaolinite was present, as the only type of clay mineral, in all kinds of parent material. The soil texture was generally sandy (Table 1), although in some cases with a greater amount of silt and clay fractions the soils were loamy (Fig. 2). The variogram (Fig. 3a) of the FF content manifested a spatial dependence at the distance ~1000 m, and had a relatively large (53%) proportion of nugget variation (C0/(C0 + C)) (Table 2). The large nugget effect is an effect of a much more scattered spatial pattern of Triassic/Quaternary deposits than suggested by the simplified geological map (Fig. 1). Because soil texture has important effects on SOC accumulation, for further analysis we decided to arrange the soils into four groups based on the topsoil's FF content: 0–10, 11–20, 26–50 and 51–100%. These ranges are related to the average and quartile values calculated for log-transformed FF contents, transformed because of strong skewness. 3.2. Forest species composition Four tree species, Silver fir (A. alba), European beech (F. sylvatica), common hornbeam (Carpinus betulus) and Scots Pine (P. sylvestris), made up about 80% of AGB. Silver fir, the dominant species, occupied 46% of the area and occurred at all types of sites (Fig. 4a). However, European beech (Fig. 4b), which occupied 25% of favoured soils derived from Triassic deposits, preferred weathered sandstones over claystones. Hornbeam and Scots pine (Fig. 4c and d, respectively) had minor shares (4% each) in ABG. Pine-dominated stands were located on sandy soils at the lowest elevations, while hornbeam reached highest shares in AGB on loamy soils. The highest average share of hornbeam (8%) was on soils having 25–50% of FF, while on soils with lower (0–25%) and higher

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Fig. 3. Empirical variogram of: a) the content of fine fraction (FF) in the topsoil horizons, b) the share of fir, beech, hornbeam and pine in aboveground tree biomass, c) the stock of soil organic carbon in organic horizons (SOCO) and mineral soil (SOCMin), d) the content of carbon in free light fraction (fLF), occluded light fraction (oLF) and mineral associated fraction (MAF) of topsoil horizons.

(50–100%) contents of FF the share of hornbeam was significantly smaller (2%). The variograms (Fig. 3b) demonstrated clear spatial autocorrelations of the share of fir, beech and hornbeam, observed in the range of about 1–1.5 km. The variogram of the share of pine had a more complex structure. 3.3. Moisture Based on the site moisture index (SMI) the investigated plots were in relatively narrow range from fresh (minimum SMI = 2.3) to moist/ wet (maximum SMI = 4.4). Average SMI for all sites was 3.4 (fresh/ moist) and did not differ between the FF groups. Only 17 sites had SMI N 3.7, which roughly means waterlogged soils. 3.4. Carbon pools in soils The total stock of C in soils (SOCT), which includes the organic and mineral horizons, ranged between 3.2 and 37.7 kg m−2, with a coefficient of variation (CV) of 44%. The average SOCT stocks did not differ Table 2 Parameters of the variogram models (as an example only shown in Fig. 3a). Property

Type

Nugget C0

Sill C0 + C

Range (m)

Refer to figure

FF Fira Beecha Hornbeama Pinea SOCO SOCMin CfLF CoLF CMAF

Spherical Spherical Exponential Spherical Sphericalb Spherical Spherical Spherical Spherical Spherical

0.54 0.73 0.62 0.78 0.52 0.59 0.79 0.75 0.51 0.68

1.02 1.01 1.05 1.10 0.78 1.08 0.96 0.94 0.95 1.06

970 910 1550 1100 450 1830 1450 1050 1080 1050

Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig.

a b

Share in AGB. Because of complex structure, the model range was limited to 1500 m.

3a 3b 3b 3b 3b 3c 3c 3d 3d 3d

significantly between the FF classes (Table 3). Spatial dependence of SOCTOT was rather weak; the structural variation was dominated (80%) by the nugget effect (not shown). On average, SOCO contributed about 50% to SOCT. The organic horizon was present at all sampling points; however, its depth varied between 1 and 33 cm, with a mean thickness of 6 cm; thus, the CV was relatively large (84%). The SOCO stock was significantly higher in sandy soils, with the topsoil FF content 0–10% (Table 3). Clearly visible spatial dependence (Figs. 3c, 6a), observed at the distance of 1830 m, had a moderate (55%) C0/(C0 + C) (Table 2). Multiple regression analysis revealed that the SOCO stocks were significantly related to forest species composition. Summarized share of beech and hornbeam explained 33% of SOCO variance (negative effect, p b 0.0001). Increasing site moisture had small, yet significant (p = 0.006), positive effects and explained another 4% of variance. However, there was no clear relationship between SOCO and SMI in a range of SMI between 2.3 and 3.7 (i.e., fresh and moist sites). Only at sites with SMI N 3.7 we observed a great increase in the depth of the O horizon. The SOCO was also significantly, negatively affected by the topsoil's content of FF and base saturation (p = 0.007 and 0.01, respectively). These two variables together explained 8% of the variation in SOCO. The mean depth of the topsoil horizon was 12 cm and varied between 3 and 30 cm. On average, 66% of SOCMin was allocated in the topsoil horizons (SOCMinT), while 34% was stored in the subsoil (SOCMinS). The SOCMin stock was largely related to the content of FF; however, in the topsoil horizons only the group of soils having 0–10% of FF had significantly lower SOCMinT stock, while in the subsoil horizons the stock of SOCMinS increased significantly with an increase of FF (Table 3). Spatial distribution of SOCMin (Fig. 3c) was somehow different for topsoil (Fig. 6b) and subsoil horizons (Fig. 6c). Spatial dependence of SOCMinT was fairly weak and the structural variation was dominated by the nugget effect (80%), while the variogram calculated for SOCMinS exhibited stronger autocorrelation at distances up to 800 m, and the contribution of nugget to the sill was smaller (48%) (Table 2). Based on the results of multiple regression analysis, the variability of SOCMin stocks was primarily explained (18% of variance) by the content

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a

95

b

c

d

Fig. 4. Maps representing the share of the major forest tree species in aboveground biomass: a) fir, b) beech, c) hornbeam, d) pine.

of FF (p b 0.0001). Another significant load to the model were derived by pHH2O (negative influence, 5% of explained variance, p = 0.008). The share of beech in AGB had small (6%), albeit significant (p = 0.022), positive influence on SOCMin, mostly the result of the significant positive influence of beech SOCMinS. 3.5. Fractions of C in the topsoil horizons All soils were very acidic (Table 4). Despite the large variability of soil texture (FF) and forest species composition, the soil pHH2O varied in a relatively narrow range (CV = 5.5%). In agreement with the high Table 3 Means and standard errors of soil organic carbon (SOC) stocks in kg m−2 in relation to the topsoil FF content. Different letters indicate statistically different averages (ANOVA, Tukey's post hoc test, p b 0.05). FF content (%)

N

SOCO

All groups 0–10 11–20 21–50 51–100

275 5.1 ± 4.3 83 6.3a ± 4.1 84 4.8b ± 3.8 88 4.4b ± 4.9 20 3.4b ± 3.8

SOCMin

SOCMinT

SOCMinS

5.3 ± 3.1 3.4 ± 2.4 1.9 ± 1.7 3.3a ± 2.5 2.3a ± 2.2 1.2a ± 1.6 5.2b ± 2.5 3.5b ± 2.3 1.7b ± 1.3 6.9c ± 3.2 4.2c ± 2.3 2.6c ± 1.9 7.9c ± 2.3 5.2c ± 2.7 2.8c ± 1.3

acidity, the content of base cations was low. Calcium, potassium and magnesium occupied only 6.1 (from 0.8 to 39.7) % of CEC. Therefore, the majority of the exchange sites were occupied by H and Al. The average pH values were not affected by the increasing FF content; however, the mean base cation content was increasing significantly with increasing FF content (Table 4). The contents of oxalate-extractable Fe (Feox) and Al (Alox) in the topsoil horizons varied in a broad range and demonstrated strong affinity to FF content (Table 4).

Table 4 Means and standard errors of the selected chemical properties of mineral topsoil horizons in relation to the FF content. Different letters indicate statistically different averages (ANOVA, Tukey's post hoc test, p b 0.05). FF content (%)

N

pH

10.5 ± 4.6 9.8 ± 4.1 10.1 ± 4.4 11.3 ± 5.1 11.4 ± 5.7

Alox

Feox

mg kg−1

SOCT All groups 0–10 11–20 21–50 51–100

275 83 84 88 20

4.20 ± 0.23 4.23 ± 0.21 4.19 ± 0.22 4.20 ± 0.26 4.24 ± 0.24

977 ± 1203 429a ± 413 911b ± 1189 1478c ± 1480 2000c ± 918

BC cmolc kg−1

3205 ± 5987 768a ± 1345 2064b ± 3345 6123c ± 8614 8531c ± 6892

0.56 ± 0.68 0.25a ± 0.19 0.46b ± 0.42 0.78c ± 0.58 2.09d ± 2.19

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Table 5 Means and standard errors of the content of carbon (Ct) and C in fractions: fLF, oLF and MAF, in topsoil horizons, in relation to the FF content. Different letters indicate statistically different averages (ANOVA, Tukey's post hoc test, p b 0.05).

Table 6 Means and standard errors of the C to N ratio in topsoil horizons, in relation to the FF content. Different letters indicate statistically different averages (ANOVA, Tukey's post hoc test, p b 0.05).

FF content (%)

N

Ct

CfLF

CoLF

CMAF

FF content (%)

N

Ct/Nt

CfLF/NfLF

CoLF/NoLF

CMAF/NMAF

All groups 0–10 11–20 21–50 51–100

275 83 84 88 20

23.3 ± 16.8 15.5a ± 13.8 23.2b ± 14.5 29.8c ± 18.8 35.5c ± 8.4

8.4 ± 9.8 6.7a ± 9.4 8.5a ± 7.8 10.6b ± 9.4 7.2a ± 3.1

5.8 ± 5.4 3.8 ± 4.4 5.8 ± 6.2 6.4 ± 5.7 4.8 ± 2.8

8.7 ± 6.3 3.1a ± 2.0 8.0b ± 3.1 13.1c ± 5.8 22.6d ± 6.1

All groups 0–10 11–20 21–50 51–100

275 83 84 88 20

21.1 ± 6.0 24.6a ± 6.6 21.7b ± 4.8 17.8c ± 3.9 15.8c ± 7.1

28.4 ± 9.2 31.1a ± 11.3 28.4b ± 7.6 26.0b ± 7.8 25.8b ± 5.9

30.4 ± 14.5 33.0a ± 15.2 33.2a ± 16.9 26.0b ± 10.1 26.5b ± 11.3

19.2 ± 5.6 22.0a ± 6.0 20.0a ± 4.9 16.6b ± 3.9 12.8b ± 3.8

Total concentration of Ct in the topsoil horizon (Table 5) was highly variable (CV = 72%). Regarding soil texture, we found that Ct was significantly smaller in soils with low (0–10 and 11–25%) FF content (Table 4). The content of Ct explained 80% of Nt variability (Nt = 0.049 × Ct, intercept = 0, n = 275). The slope of the Ct to Nt relationship was 0.036, 0.046, 0.051 and 0.099 for soils having 0–10, 11–25, 26–50 and 51–100% of FF, respectively; thus the C/N ratio was decreasing on the same order. The majority of the CfLF was recognized (the microscope check) as fine root fragments. The content of C in this fraction was relatively high (28.5 ± 5.5%). Nevertheless, because it comprised only a small share of the total sample weight, the CfLF was only 34.8% (from 0 to 79%) of Ct. The average content of CfLF was little dependent on the FF content, i.e., the CfLF was significantly higher only in the group of soils containing 26–50% of FF (Table 5). The variogram of CfLF content (Fig. 3d) showed fairly weak spatial dependence (range ~ 1600 m) with large contribution (80%) of the nugget variance (Table 2). Fig. 6d presents a kriged map of the spatial distribution of CfLF. The crossvariograms confirmed that the content of CfLF in topsoil was spatially positively correlated with the content of FF and the share of beech in AGB (Fig. 5b and c, respectively); it was negatively correlated with

b 1

Cross semivariance

Cross semivariance

a

SOCO (not shown). In addition, the results of multiple regression showed weak, yet significant, positive correlations between the content of CfLF and share of beech in AGB, and the FF content. The C/N ratio in the LF (Table 6) was higher than the C/N determined for the non-fractionated samples (Table 4). The average C/N of this fraction was significantly higher only in sandy soils (0–10% of FF), while for the other FF groups the averages were similar (Table 6). Carbon accumulated in oLF (CoLF) was 25.4% of Ct. The average content of the CoLF was not correlated with the FF content (Table 5). The multiple regression model was able to explain in total 35% of the variation in CoLF, with the significant explanatory variables Alox/Ct ratio (p = 0.03), Feox/Ct ratio (p = b 0.001) and the share of fir in AGB (p = 0.003). The variogram of CoLF (Fig. 3d) showed a relatively strong autocorrelation with a 54% share of nugget variation. Spatial distribution of CoLF was demonstrated as a kriged map (Fig. 6e). The CoLF/NoLF ratio (Table 6) was higher than the Ct/Nt ratio determined for the nonfractionated samples (Table 4) and was similar to the CfLF/NoLF ratio. The average CoLF/NoLF did not differ between the FF groups. The CMAF, which constituted on average 39.7 (from 0 to 89.1) % of Ct, was strongly related to the content of FF. As a consequence, the spatial distribution of CMAF (Fig. 6f) was highly correlated to the FF content

0 SOCo x FF SOCMin x FF SOCT x FF

-1 0

1000

1

0 C fLF x FF C oLF x FF C MAF x FF

-1

2000

3000

0

Separation distance [m]

2000

3000

Separation distance [m]

d

c

0.3

0.0

Fir x CfLF Beech x CfLF

-0.3

Cros semivariance

0.3

Cross semivariance

1000

0.0 Fir x CoLF Beech x CoLF

-0.3 0

1000

2000

Separation distance [m]

3000

0

1000

2000

3000

Separation distance [m]

Fig. 5. Empirical cross-variograms between a) the SOCO, SOCMin and SOCT × the content of FF, b) the content of carbon in the separated fractions: CfLF, CoLF, CMAF × the content of FF, c) the share of beech in AGB × CfLF, d) the share of fir in AGB × CoLF. Acronyms: AGB, aboveground biomass; CfLF, free light fraction of SOC; CMAF, mineral associated fraction of SOC; CoLF, occluded light fraction of SOC; FF, fine fraction of soil carbon pools; SOC, soil organic carbon; SOCMin, SOC accumulated in mineral soil; SOCO, SOC accumulated in the organic horizon; SOCT, total organic carbon stock.

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a

d

b

e

c

f

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CMAF(FF 0-20%)= 0.53 FF(%) - 0.25, n=185, r 2 =0.65 CMAF(FF >20%)= 0.33 FF(%) + 3.76, n =90, r 2 =0.45

Fig. 7. The relationship between the fine fraction content (FF) and the content of C in the mineral associated fraction (CMAF). Different symbols: ○ and □ indicate samples with the FF content 0–20 and N20%, respectively. Trend line with breakpoint at 20% of FF fitted using piecewise regression.

(Fig. 5b). Regression analysis showed straight linear relationship between CMAF and FF contents, where: h i −1 2 CMA F g kg ¼ 1:4 þ 0:40  FF½%; n ¼ 275; R ¼ 0:70;

ð4Þ

however, the slope of the relationship (piecewise regression) for soils with an FF content b 20% was 0.57, while for soils with an FF N 20% the slope was 0.38 (Fig. 7). Among the soil parameters being tested, only Alox/Ct significantly (7%, p b 0.001) contributed to the explanation of CMAF variation. The CMAF/NMAF ratio (Table 6) was lower than the Ct/Nt ratio (Table 4), and lower than CfLF/NfLF and CoLF/NoLF ratios. The average CMAF/NMAF decreased significantly with increasing FF content. 4. Discussion Geospatial analysis of the SOCO and SOCMin stocks contributes to the other observations (e.g., Vesterdal et al., 2013), that forest floor C stock offset the mineral soil C. We assume, also based on our previous observations (Gruba et al., 2014) that in the landscape scale, where sites have similar moisture, the proportion between SOCO and SOCMin is primarily driven by soil texture. Therefore, the knowledge of SOCO and SOCMin, especially the ratio between these two variables, gives more valuable ecological information about the forest site quality and stability of SOC, than does SOCT alone. Results of multiple regression analysis demonstrated that variability in SOCO can be partly explained by the other than soil texture stand characteristics, such as moisture, and forest species composition. Approximated using plant indicators the current site moisture (SMI) showed that relatively small increase in site wetness does not have a significant effect on accumulation of organic layer. The rapid increase in SOCO can be attributed to the sites where SMI exceeds value of 3.7 (waterlogged sites). The positive effect of an increasing share of beech and hornbeam in AGB on SOCO was relatively clear. Several authors have reported that various tree species have a significant effect on C in the organic horizon. For example, Gurmesa et al. (2013) reported that the SOCO stocks were decreasing in the following order: oak larch N spruce N beech N oak. Oostra et al. (2006) place several forest species in sequence based on their increasing effect on the organic layer: spruce N hornbeam N oak N beech N ash N elm. Recent investigations suggest that the thinner organic layer that tended to accumulate under beech and hornbeam dominated stands can be attributed rather

to favourable environmental conditions (Berger and Berger, 2012) rather than to the litter quality and its recalcitrance (Vesterdal et al., 2012). Nevertheless, in our investigations the correlation coefficient between FF and SOCMin was relatively low (R2 = 0.22); the crossvariogram (Fig. 5a) demonstrated a clear positive spatial correlation between these two variables. A large proportion of the nugget variation (about 50%) suggests that the correlation between SOCMin and FF already occurred on a finer spatial scale than the minimum separation distance (200 m) in this study. At a fine spatial scale, the heterogeneity of SOC stocks is related to the landscape position (Spielvogel et al., 2009), individual tree positions (Schöning et al., 2006; Paluch and Gruba, 2010; Spielvogel et al., 2014) and uprooting of trees during windthrow (e.g., Šamonil et al., 2011). Soil moisture, in the investigated range, had no significant influence on SOCMin. Despite strong influence of soil texture, moisture and relatively large variability of the results, we found significant influence of trees on SOC stocks, pronounced most significantly in the case of beech. Finzi et al. (1998) and Gurmesa et al. (2013) previously indicated the presence of weak, yet significant, positive influences of broadleaves (beech and hornbeam) on SOCMin. The positive influence of beech compared with the other species, especially coniferous, can be related to greater rootlitter production and deeper rooting system (Helmisaari and Makkonen, 2002; Oostra et al., 2006). Density fractionation revealed that topsoil organic matter occurs in approximately similar portions of CfLF (35%), CoLF (25%) and CMAF (40%); however, quantitative relationships between the fractions were strongly related to the FF content (Table 6). The contribution of both LFs (fLF and oLF) is relatively high; Grüneberg et al. (2013) reported a 37% contribution of CoLF and CoLF to bulk soil C under German beech forests. An analysis of nitrogen content in relation to C was used as a tool for qualitative assessment of the fractionated SOM pools. We found that the C/N ratio was decreasing in the order of: fLF N oLF N MAF. Geospatial analysis of the data, i.e., the cross variogram CfLF × FF presented in Fig. 5b and the direct comparison of the kriged maps of CfLF (Fig. 6d), and the FF content (Fig. 2) implies that a positive relationship exists between these variables. Grüneberg et al. (2013) previously observed a significant positive correlation between the content of CfLF and FF in forest soils, and also related this observation to the higher moisture content of clay-rich soils. Based on the microscopy recognition of plant fragments in fLF, we suggest that the elevated CfLF content in clay-richer soil is primarily the result of the higher productivity of the root litter observed here. A negative spatial correlation between the organic horizon (SOCO) and the CfLF suggests that mineral soil CfLF contributed to the offset between SOCO and SOCMin. The latter contradicts the

Fig. 6. Map of the carbon stock (kg m−2) in: a) organic horizon (SOCO), mineral topsoil (SOCMinT), c) mineral subsoil (SOCMinS), and maps of carbon content (g kg−1) in physically separated fraction of topsoil horizons: d) free light fraction (CfLF), e) occluded light fraction (CoLF), f) mineral associated fraction (CMAF).

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conclusion of Grüneberg et al. (2013), who suggested that a positive relationship exists between the forest floor and CfLF. Multiple regression and spatial analysis (Fig. 5c) both demonstrated the positive effect of beech on CfLF. The CfLF content explained 89% of the variation in nitrogen content (NfLF) in this fraction. Based on Gruba et al. (2014), the C/N ratio of the organic horizon sampled in pure beech, fir and hornbeam stands were 29, 22 and 22, respectively. In addition, data presented in Augusto et al. (2002) implies that fir and beech had similar C:N ratios. Therefore, not surprisingly, the forest species composition had no significant influence on the CfLF/NfLF ratio. Intriguingly, statistical and geostatistical analysis showed that CoLF was not related to the FF content, but was significantly positively influenced by the Alox/Ct and Feox/Ct ratios. In addition, the comparison of Figs. 4a and 6e implies that some similarities exist in the spatial distribution of the share of fir in AGB and CoLF content. Also, the cross-variogram in Fig. 5d confirms that a positively correlated relationship exists between an increasing share of fir biomass and the content of CoLF in mineral topsoil. The increased Al and Fe saturation of SOM is presumably preferential for the occlusion process via cation bridging between SOM and minerals as suggested by Hobbie et al. (2007) and Mueller et al. (2012). Thus our results confirm that soil acidity and metal saturation of functional groups plays a key role in stabilization of SOM. In the range of acid soils (pH b 4.5) dominates the saturation by Al and Fe, while the base (Ca, Mg) is dominant cations in soils with pH N4.5 (Gruba et al., 2013). Not surprisingly, CMAF was highly dependent on FF. From the slope of the linear relationship for the entire dataset, not shown in Fig. 7, we conclude that 1% of FF adsorbs 0.4 g of C, which is higher than the values reported by Six et al. (2002) for 1:1 clays (0.24 g C per 1% of FF) and for forest soils (0.2 g C per 1% of FF). An even steeper slope (0.53) was found for soils with the FF content below 20%, while for an FF content of N 20% the slope was 0.33. Our results suggest that, apart from the FF content, metal (Al) saturation has also an enhancing effect on the CMAF content. 5. Conclusions Accumulation of C in soils in the landscape scale is affected by a variety of site factors. The variation in the FF content had a major influence on differences in accumulation of SOC in soils, particularly on its partitioning between the organic horizon and mineral soil. In soils richer in the FF, more SOC was accumulated in mineral horizons and less in the organic horizons. Site moisture had limited influence on SOC accumulation. The stocks of carbon in the organic layers were significantly greater at wettest sites only, while the site moisture had no significant effect on the amount of carbon in mineral horizons. Accumulation of SOC in mineral soil was also positively affected by the degree of SOM saturation with metal, especially Al. Concerning tree species composition, the increasing share of beech influences the distribution of C stock in soil profiles, reducing the depth of O horizon and increasing C stored in mineral soil. The detailed investigations of the physically fractionated samples of mineral topsoil horizons revealed that the content of organic matter adsorbed on the surface of mineral silt and clay was greater than the values reported previously for forest soils containing 1:1 type clay minerals. Moreover, presumably due to smaller saturation of soil with SOM, soils with a low FF content (b 20%) adsorbed relatively more C, than FFrich soils (N20%) derived from Triassic deposits. We found that the content of FF had enhancing effect for accumulation of C as MAF, but also fLF. Aside from the FF content, the content of stable CMAF and CoLF was influenced by metal saturation, especially Al, confirming, that Al ions play an important role in the stabilization of SOM inside aggregates. Forest tree species had significant effect on the content of CfLF and CoLF. Since the majority of the CfLF was recognized as root fragments, the positive effects of beech on the CfLF were probably related to the development of its root system. The positive effects of fir on the CoLF content likely

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can be attributed to the higher saturation of fir-derived SOM with Al than SOM in stands of deciduous species.

Acknowledgements The project was financed by the National Science Centre, Poland; Decision No. DEC-2011/01/B/NZ9/06879.

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