Soil microbial community response to variation in vegetation and abiotic environment in a temperate old-growth forest

Applied Soil Ecology 68 (2013) 10–19

Contents lists available at SciVerse ScienceDirect

Applied Soil Ecology journal homepage: www.elsevier.com/locate/apsoil

Soil microbial community response to variation in vegetation and abiotic environment in a temperate old-growth forest Erika Gömöryová ∗ , Karol Ujházy, Michal Martinák, Duˇsan Gömöry Technical University in Zvolen, Faculty of Forestry, TG Masaryka 24, SK-96053 Zvolen, Slovakia

a r t i c l e

i n f o

Article history: Received 29 June 2012 Received in revised form 4 March 2013 Accepted 14 March 2013 Keywords: Soil functional diversity Microbial activity Plant diversity Natural forest

a b s t r a c t Changes of soil microbial community caused by the heterogeneity of abiotic and biotic environment were studied in the reserve Dobroˇc, Slovakia. Data on vegetation, microclimate, soil properties and microbial activity were collected on two linear transects crossing both the core of the reserve and the buffer zone. In contrast to expectations, the variation of most environmental variables was comparable or even higher in the buffer zone than in the old-growth forest. Beta diversity was much higher in the natural forest, which coincided with differentiation patterns of trees and understory plants. Mantel correlations between microbial community indicators and environmental variables showed that soil chemistry and vegetation diversity were the most important determinants of microbial activity. Redundance analysis of microbial data identified potassium content, plant richness and influence of fir as the drivers of functional group composition. Inconsistency of correlations of microbial community characteristics with environmental variables indicates that different processes associated with the demography and functions of microbiota are driven by different environmental factors. Amount and variety of substrates available for decomposition seems to influence microbial community more than microclimate. © 2013 Elsevier B.V. All rights reserved.

1. Introduction Positive and negative feedbacks between soil microbiota and vegetation have been documented by a plenty of both observational and manipulative experiments (Bever, 2003; Ehrenfeld et al., 2005; Miki et al., 2010; Pregitzer et al., 2010). Interactions of aboveand belowground components of ecosystems are generally considered essential to ecosystem functioning (Zak et al., 2003; Wardle et al., 2004; Bardgett et al., 2005). As a result, soil microbiota has frequently been found to be spatially associated with the composition, richness and biomass of plant communities (Pennanen et al., 1999; Saetre and Bååth, 2000; Mahaming et al., 2009). Trees as long-lived organisms representing the major portion of biomass in forest ecosystems affect both above- and belowground ecosystem components, including soil microbial community. Trees produce the majority of litter deposited on the ground in a forest, as well as a substantial portion of root exudates and dead roots below the ground. Providing the matter decomposed by soil microorganisms, trees influence soil microbiota essentially in the same way as other plants, but their effect is potentially stronger because of a greater biomass. Moreover, trees redistribute soil resources by

∗ Corresponding author. Tel.: +421 45 5206214/907 882912; fax: +421 45 5332654. E-mail address: [email protected] (E. Gömöryová). 0929-1393/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.apsoil.2013.03.005

drawing water and nutrients from the rhizosphere and depositing them under or close to their canopy (Gibson, 1988; Pärtel and Wilson, 2002). Their roots directly interact with soil microorganisms (Stoyan et al., 2000; Wilkinson and Anderson, 2001). Indirectly, trees influence soil microbial communities by modifying the access of solar radiation and precipitation water to the soil (crown interception, stemflow, changes of rainwater chemistry) (Chen et al., 1999). Finally, trees affect the composition of understory vegetation, representing another community interacting with soil microorganisms (Ujházy et al., 2013). The tree layer of natural (old-growth) forests is generally considered to exhibit a higher species and structural diversity compared to managed stands. Small-scale changes of density, age, diameter and height structure of trees typical for forests developing naturally without human interferences are expected to yield a higher environmental heterogeneity and a higher diversity of associated plant and animal communities (Bobiec, 1998; Ferris-Kaan et al., 1998; Bublinec and Pichler, 2001; Sullivan et al., 2009). This view was challenged as too mechanistic (Korpel’, 1995; Franklin et al., 2002; Paillet et al., 2010). On one hand, the structure of a temperate natural forest changes among different ontogenetic stages of a forest community, and horizontal closed canopy accompanied with relatively homogeneous microsite conditions appears in some types of natural forests (e.g., pure beechwoods or montane spruce forests) at certain developmental phases (Korpel’, 1995). On the other hand, forest stands can be managed in different ways and

E. Gömöryová et al. / Applied Soil Ecology 68 (2013) 10–19

some silvicultural systems (e.g., plenter or irregular shelterwood system) yield structurally diverse stands (Matthews, 1989; Otto, 1994). In spite of this, the idea of natural forests as harbours of diversity still persists. Hypothetically, high species and structural diversity of the tree layer in a natural forest should be reflected in high heterogeneity of soil microbial communities. At the same time, it should be associated with broad ecological gradients, allowing identification of relationships of the soil microbial community and the environment. Therefore, the objectives of this study were the following: (1) assessing the relationships between soil microbial community characteristics and environment variables in a temperate old-growth forest and (2) assessing the differences in patterns and levels of variation of soil microbial community characteristics between a natural forest and a managed stand. A similar study using nearly the same methodology was done related to secondary succession of abandoned grassland communities towards a forest (Gömöryová et al., 2009). We also evaluated the consistency of microbiota–environment relationships between these two studies. 2. Material and methods 2.1. Study site The study site is the nature reserve Dobroˇcsky´ prales situated in Central Slovakia (48◦ 40 37 N, 19◦ 40 34 E, approximately 885–965 m a.s.l.), surrounded by a buffer zone. This natural forest represents one of the oldest and best preserved montane forests in Slovakia; the core zone of the reserve has been protected and nearly untouched at least since the reserve was declared in 1913 (and probably even earlier, as it is situated quite far from the nearest settlements). The study site is located on a north-facing slope with a 10% inclination. The natural forest is composed mainly of common beech (Fagus sylvatica), silver fir (Abies alba), Norway spruce (Picea abies), maples (Acer pseudoplatanus, A. platanoides), European ash (Fraxinus excelsior) and Wych elm (Ulmus glabra). The structure of the buffer zone has been changed; autochthonous stands were replaced by spruce plantations forming relatively homogeneous even-aged mature stands at present. However, a continuous beech undergrowth of up to 5 m height originating from natural regeneration appears in gaps. Mean temperatures in the area reach 4.5–5.8 ◦ C and annual precipitation is approximately 840–940 mm. The soils are deep cambisols developed from the bedrock of biotite granodiorite (Korpel’, 1995). In spring 2009, two permanent transects (663 × 16 m and 533 × 16 m) were established in the eastern and western part of this area. The transects crossed all stages of the natural forest development cycle and extend into the buffer zone. Along the transects, 94 square plots of the area 2.25 m2 spaced 13 m were established and mapped using GPS and FieldMap (IFER, 2008), out of which 47 were placed in the reserve core (old growth) and 47 in the buffer zone (managed forest). 2.2. Vegetation and site condition assessment Field-layer vegetation was recorded on a more detailed grid formed of 9 squares of 0.25 m2 within each plot. For each plot, we estimated the frequency of vascular plants (E1 ; excluding tree seedlings) and bryophytes (E0 ) based on their presence/absence on the 0.25 m2 plots, as well as the percentage cover of the E0 and E1 layers. To assess the influence of tree species on sampling plots, positions and breast-height diameters (dbh, at 1.3 m height) of all trees >1.3 m tall were recorded within a 10 m neighbourhood of the sampling plot centre. Tree influence potential based on the sizes

11

of trees inversely weighted by their distances from the plot centre was calculated following Kuuluvainen and Pukkala (1989): IPBA =



i

BAi e−di

where BAi is the basal area (area of the cross-section of the stem at the breast height) of the ith tree and di is its distance from the sampling plot centre. The influence potential was calculated as a single-plot-level statistic separately for each tree species (common beech, silver fir, Norway spruce) or group of species (noble hardwoods). At each plot, canopy light transmission was measured using vertical hemispherical photographs taken 20 cm above the soil surface with a Nikon Coolpix 5400 digital camera equipped with a fisheye FC ER9 objective. Canopy openness (the percentage of open sky seen from beneath the forest canopy), and the amount of direct and diffuse solar radiation transmitted by the canopy were estimated from the photographs using Gap Light Analyser 2.0 (Frazer et al., 1999). To have an approximate idea of temperature distribution along the transects during the vegetation season, soil temperatures at the depth of 5 cm were measured using Hg soil thermometers on two dates: May 21 and July 16, 2009. Temperatures were recorded once per hour, whereby the average temperature (TAVG ) for each plot was calculated and averaged over the two dates. 2.3. Soil properties and microbial analysis At the edge of each plot, surface organic material was collected from two plots of 0.0625 m2 . Samples were dried at 60 ◦ C and weighted to assess the amount of organic matter in this horizon. Soil samples for the analyses of physical and chemical properties and microbial community were taken from the uppermost mineral A-horizon (from the depth of 0 to 10 cm) as a mixture of three subsamples (200 g each) from each plot on September 18, 2009. A part of each sample was air-dried and used for measurements of physical and chemical properties. The other part of soil sample, used for microbial analyses, was stored in field-moist condition at 4 ◦ C until laboratory analyses were performed. Soils were not sieved to preserve natural conditions for microbiota. However, rocks, roots or larger organic debris were hand-picked removed. Soil water content was determined gravimetrically by ovendrying fresh soil at 105 ◦ C for 24 h. Soil acidity was measured in 1 M KCl suspension (20 g soil plus 50 ml KCl solution) after 24 h potentiometrically. For the determination of total carbon, nitrogen and sulphur content VarioMacro CNS Analyser was used. Exchangeable calcium, magnesium and potassium were estimated in NH4 Cl extract using atomic absorption spectrometry (GBC Avanta AAS). Basal soil respiration was measured by estimating the amount of carbon dioxide released from 50 g of fresh soil after a 24 h incubation at 22 ◦ C and absorbed in 25 ml 0.05 N NaOH. The amount of carbonate was determined by the titration with 0.05 N HCl after the precipitation of carbonates by 5 ml BaCl2 . Catalase (EC 1.11.1.6) activity was measured 10 min after 20 ml 3% H2 O2 was added to 10 g fresh soil sample based on the volume of discharged oxygen according to the method of Khaziev (1976). N mineralization was determined using the laboratory anaerobic incubation procedure described by Kandeler (1993). Soil samples (5 g) under waterlogged conditions were incubated at 40 ◦ C for 7 days to prevent nitrification, and NH4 -N was measured by a colorimetric procedure. Microbial biomass was assessed using the microwave-irradiation procedure following Islam and Weil (1998): 10 g oven-dried equivalent (ODE) of field-moist soil adjusted to 80% water-filled porosity was irradiated twice by microwave (MW) energy at 400 J g−1 ODE soil to kill the microorganisms. The time settings and MW oven power depended on the total amount of soil in the MW oven. After cooling, soil samples were extracted with 0.5 M K2 SO4 . C content in

12

E. Gömöryová et al. / Applied Soil Ecology 68 (2013) 10–19

the extract was quantified by the oxidation with K2 Cr2 O7 /H2 SO4 and titrimetrically by (NH4 )2 Fe(SO4 )2 . The same procedure was done with a non-irradiated sample. The microbial biomass carbon was then determined as Cmic =

Cirradiated − Cnon-irradiated , KME

whereby extraction efficiency factor KME = 0.213. Community-level metabolic profiles of microbial communities were determined in two ways—employing BIOLOG® EcoPlates (Insam, 1997) and using a rapid microtiter-plate method to measure the evolved CO2 using whole soil (Campbell et al., 2003). BIOLOG Eco Plates contain 31 different organic substrates. In each well there is also the redox dye tetrazolium, which is reduced to insoluble blue coloured formazan by respiration. The rate and extent of colour formation indicate the rate and extent to which respiration occurs with the substrate present in the well. Inocula were prepared by resuspending fresh soil in 0.85% NaCl, centrifuged (1000 rpm for 5 min.), the supernatant was diluted 1:10,000, and 150 ␮l of extract were incubated in microtitration plates at 27 ◦ C for 5 days. Absorbance at 590 nm was recorded every 12 h using the Sunrise Microplate reader (Tecan, Salzburg, Austria). Absorbance values were blanked against the control well. The metabolic activity was calculated as the area below the time–absorbance curve, and was used as a measure of the abundance of the respective functional group. The method according to Campbell et al. (2003) relies on the colorimetric measurement of CO2 evolved from carbon substrate amendment soil samples placed in deep-wells of microtiter plates. 300 mg of soil (60% WHC) was placed into a deep-well (1.2 ml) and amended with 25 ␮l of C source. Water solutions (30 mg ml−1 ) of the following substrates were used: ␣-ketoglutaric acid, l-arginine, l-asparagine monohydrate, cellulose, dl-malic acid, d-(−)-methylglucamine, l-phenylalanine, l-serine, starch, Tween 80, d-(+)-xylose. The plate was immediately covered by microtiter plate with gel (Noble agar 1%) containing a pH indicator dye–cresol red (12.5 ␮g g−1 ), potassium chloride (150 mM) and sodium bicarbonate (1.5 mM). The detection plate was read immediately before and after 6 h of incubation at 25 ◦ C with a Sunrise Microplate reader (Tecan, Salzburg, Austria) at 590 nm. The absorbance after 6 h was normalized for any differences recorded at the first reading before exposure. The absorbance values were converted to the CO2 amount using the calibration curve. A similar procedure was used for the estimation of the relative contribution of fungi and bacteria to glucose-induced respiration in soil. Streptomycin sulphate and cycloheximide (4000 ␮g g−1 soil) were added to soil in deep wells to inhibit bacteria and fungi, respectively. 2.4. Data analysis For the assessment of the alpha diversity of soil microbial functional groups based on the Biolog approach, we used Hill’s index (Hill, 1973): N2 =

1



p2 i i

where pi is the frequency (relative abundance) of the ith functional group. As activity was observed in nearly all samples on all substrates (although very weak in some cases) and we did not want to introduce any artificial limit as a presence/absence indicator, richness of microbial functional groups was not assessed. Species richness of the understory vegetation was assessed as the number of vascular plant and bryophyte species present on at least one 0.25 m2 subplot of the 2.25 m2 sampling plot. Alpha diversity was also assessed using the Hill’s index, whereby the species’

frequencies were calculated based on the presence/absence in the subplots. Tree species richness was assessed as the number of tree species present in the 10 m neighbourhood of the point. For calculation of alpha diversity, Hill’s index was used; species’ frequencies were based on basal areas of trees. To assess the beta diversity, we used the concept of Jost (2007) based on Gini–Simpson coefficients: Hˇ =

Htot − H˛ 1 − H˛

where Htot = 1 −



p¯ 2 i i

and H˛ =

 j

1−



p2 i ij

 /n; pij and p¯ i

being the frequency of ith species or functional group at the jth plot and average frequency over the whole reserve core and/or buffer zone, respectively, and n being the number of plots. As the distributions of microbial parameters frequently deviated from the normal distribution and variances differed between the two transects and statuses of forest (old-growth vs. managed stand, see Table 1), differences of environmental and microbial variables were tested by non-parametric Wilcoxon tests using the SAS procedure NPAR1WAY (SAS, 2009). To assess the relationships between the activities of the soil microbial community and the environment, we used both univariate and multivariate approaches. In the former case, we calculated correlations between microbial community indicators and environmental variables. Because data (at least the descriptors of the environment) were not spatially independent, we used partial Mantel tests (Smouse et al., 1986) yielding partial correlation coefficients between the matrices of differences in response (microbial) and predictor (environmental) variables under consideration of spatial distances among observation units (plots). The significances of the partial correlation coefficients were tested using 100,000 random permutations. The calculations were performed using the program zt (Bonnet and Van de Peer, Ghent University, Belgium). Because large correlation matrices were produced, the significances were corrected using sequential Bonferroni procedure (Quinn and Keough, 2002). To assess how the composition of functional groups of microorganisms (Biolog assay) is related to the environment, a direct gradient analysis (redundancy analysis; RDA) was performed using ˇ CANOCO v.4 (ter Braak and Smilauer, 2002), allowing to determine environmental variables which best explain the changes of the frequency distributions of microbial functional groups along the transects. RDA is based on a linear approximation of the species’ response to environmental gradients (appropriate under relatively homogeneous environmental conditions indicated by a small gradient length; prior to the analysis, we tested the gradient length by detrended correspondence analysis) and yields constrained ordination axes reflecting the direction of the maximum variability within the dataset, that can be explained ˇ by the assessed environmental factors (ter Braak and Smilauer, 2002). The significance of environmental variables and RDA axes was tested using Monte-Carlo permutation test (9999 runs). Significant environmental variables were identified by forward selection. In the case of the assay according to Campbell et al. (2003), respiratory activity after addition of different substrates cannot be readily interpreted as a measure of the abundance of the respective microbial group. Therefore, a simple principal component analysis (PCA) was performed on this dataset (including glucose-induced respiration under streptomycin and cycloheximide treatment) using the PRINCOMP procedure of SAS.

E. Gömöryová et al. / Applied Soil Ecology 68 (2013) 10–19

13

Table 1 Overview of the measured microbial community characteristics and environmental variables (least-square estimates of means and standard deviations). Variable

Abbr.

Old-growth

Managed stand

Mean ± S.D.

Mean ± S.D.

Diffa

Varb

ns * ns ns ns

ns ns ns ns ns

***

***

Microbial activity indicators Microbial carbon (␮g C g−1 ) Catalase activity (ml O2 g−1 min−1 ) Basal respiration (␮g CO2 -C g−1 h−1 ) N mineralization (␮g NH4 -N g−1 7 day−1 ) Metabolic quotient (␮g CO2 -C mg C g−1 h−1 )

Cmic Cat BR Nmin qCO2

Biolog assay Functional ␣-diversity

Fdiv

Rapid microplate assay Cycloheximide assayc (␮g CO2 -C g−1 h−1 ) Streptomycin assayc (␮g CO2 -C g−1 h−1 ) log S/Cd ␣-Ketoglutaric acid (␮g CO2 -C g−1 h−1 ) Arginine (␮g CO2 -C g−1 h−1 ) Asparagine (␮g CO2 -C g−1 h−1 ) Cellulose (␮g CO2 -C g−1 h−1 ) Malic acid (␮g CO2 -C g−1 h−1 ) Methylglucamine (␮g CO2 -C g−1 h−1 ) Phenylalanine (␮g CO2 -C g−1 h−1 ) Serine (␮g CO2 -C g−1 h−1 ) Starch (␮g CO2 -C g−1 h−1 ) Tween 40 (␮g CO2 -C g−1 h−1 ) Xylose (␮g CO2 -C g−1 h−1 )

Chex Stre lSC Ket Arg Asp Cell Mal Metg Phe Ser Sta Tw40 Xyl

26.77 25.15 −0.040 165.83 79.25 240.36 52.77 40.00 0.66 0.93 33.30 10.77 14.56 1.97

± ± ± ± ± ± ± ± ± ± ± ± ± ±

11.86 15.17 0.391 108.32 93.94 218.94 27.41 9.43 0.35 1.60 10.50 4.42 4.43 2.36

24.27 17.43 −0.173 147.56 63.28 164.95 51.86 26.57 1.37 2.26 37.74 12.10 16.09 12.80

± ± ± ± ± ± ± ± ± ± ± ± ± ±

16.28 13.05 0.522 118.24 97.51 173.73 32.18 11.50 1.38 5.84 17.38 14.15 8.40 25.16

* ns ns ns ns ns ns *** ** ns ns ns ns ***

* ns ns ns ns ns ns ns *** *** *** *** *** ***

Soil chemical properties Moisture (% w/w) pH/KCl C (mg g−1 ) N (mg g−1 ) C:N ratio Ca (␮g g−1 ) Mg (␮g g−1 ) K (␮g g−1 )

Moist pH C N C/N Ca Mg K

28.72 3.47 60.67 4.49 13.42 1434.9 233.90 141.01

± ± ± ± ± ± ± ±

9.42 0.17 16.32 0.95 1.43 627.7 51.97 114.47

25.81 3.45 65.01 4.54 14.26 1213.6 217.65 87.03

± ± ± ± ± ± ± ±

7.34 0.16 14.68 0.78 1.53 425.7 35.58 42.47

ns ns ns ns * ns ns ns

ns ns ns ns ns ** * ***

Light and temperatures Canopy openness (%) Diffuse radiation (mol m−2 day−1 ) Direct radiation (mol m−2 day−1 ) Average temperature (◦ C)

CO Rdiff Rdir Tavg

7.69 1.90 2.23 12.04

± ± ± ±

2.76 0.99 0.83 0.61

10.29 2.55 3.06 12.76

± ± ± ±

4.93 1.94 1.53 0.64

ns * ns ***

*** *** *** ns

Trees IPBA beech IPBA noble hardwoods IPBA fir IPBA spruce IPBA sum Tree richness Tree diversity Litter mass (kg m−2 )

IPb IPn IPf IPs IPt Trich Tdiv Litt

147.58 5.79 176.12 39.72 369.21 3.03 1.92 1.33

± ± ± ± ± ± ± ±

275.49 17.44 579.39 172.84 643.36 1.11 0.59 0.46

48.64 7.91 0.13 151.15 207.99 3.08 1.40 1.42

± ± ± ± ± ± ± ±

88.55 24.58 0.78 156.43 136.01 1.24 0.52 0.51

** ns *** *** *** ns ns ns

*** * *** ns *** ns ns ns

Ground vegetation Plant richness Plant diversity E0 (bryophytes) cover (%) E1 (herbs) cover (%)

Vrich Vdiv E0 E1

7.71 5.41 25.43 1.10

± ± ± ±

3.42 2.32 19.64 2.35

10.69 7.24 46.33 6.18

± ± ± ±

4.18 2.98 28.91 15.89

** * *** ns

ns ns * ***

614.82 1.032 0.050 101.87 0.083

± ± ± ± ±

191.86 0.255 0.022 26.19 0.031

12.87 ± 3.01

682.06 1.141 0.056 95.40 0.085

± ± ± ± ±

194.19 0.226 0.024 24.19 0.033

15.16 ± 1.39

a

Significances of the pairwise Wilcoxon tests between the old-growth and managed forest: ns, non-significant (P > 0.05), *0.01 < P < 0.05, **0.001 < P < 0.01, ***P < 0.001. Significances of the F-tests of equality of variances between the old-growth and managed forest c Glucose-induced respiration rate under the treatment by cycloheximide and streptomycin, respectively. d log (ratio of streptomycin-to-cycloheximide glucose-induced respiration rate); positive and negative values indicate predominance of fungi and bacteria in the respiratory activity, respectively b

3. Results 3.1. Differences between old growth and managed stand A basic overview of the investigated microbial and environmental variables is given in Table 1. Actually, our results did not confirm the hypothesis of a higher environmental heterogeneity in the old-growth forest compared to managed stands. Variances significantly differ between the reserve core and the buffer zone for several variables, but the differences were inconsistent. Light availability varied more in the managed stand, whereas tree

influence of dominant tree species (beech, fir) was more heterogeneous in the old growth. Wilcoxon tests showed that there are almost no differences in soil chemistry, except a better humus quality in the reserve core compared to the buffer zone. Fluctuation of tree species compositions causes differences in the species-related tree influence potentials between types of forest. In spite of altered tree-species composition in the buffer zone, there is no difference in tree-species richness or diversity. However, stands are more open, and more diffuse radiation reaches the ground in the buffer zone, what is reflected in higher average temperatures (Table 1). In contrast to the expectation, ground vegetation diversity and cover are

14

E. Gömöryová et al. / Applied Soil Ecology 68 (2013) 10–19 Table 2 Significances of Wilcoxon tests of differences in the abundances of functional microbial groupsa based on the Biolog assay between the old growth and the managed stand. Substrate

s2

s3

s4

s5

s6

s7

s8

s9

s10

Diffb

– ns

M ***

M ***

M ***

– ns

M **

M ***

M **

– ns

Substrate

s11

s12

s13

s14

s15

s16

s17

s18

s19

s20

Diffb

– ns

M **

M ***

– ns

M ***

– ns

M *

M **

– ns

M ***

Substrate

s21

s22

s23

s24

s25

s26

s27

s28

s29

s30

Diffb

M **

M **

M **

– ns

– ns

M *

M **

M *

– ns

M **

Substrate

s31

s32

– ns

– ns

b

Diff

Significance labels: ns, non-significant (P > 0.05), *0.01 < P < 0.05, **0.001 < P < 0.01, ***P < 0.001. a For substrates, see Fig. 2. b M, higher average abundance in the managed stand. Fig. 1. Principal component analysis of respiration rates induced by 13 organic substrates based on the rapid microtiter-plate assay. R, reserve core (old growth); BZ, buffer zone (managed forest); E, eastern transect; W, western transect.

significantly higher in the buffer zone compared to the reserve core. On the other hand, plant communities are more differentiated in the reserve core than in the buffer zone, beta diversity being 0.589 and 0.504, respectively. The same applies to tree species composition, which shows the same pattern (beta diversities of 0.446 and 0.285, respectively). In the case of microbial community descriptors, few differences were observed between the core and the buffer zone of the reserve. This applies also to substrate-induced respiration in the rapid-microtiter-plate assay. Surprisingly, the observed significant differences were not consistent: higher catalase activity, more rapid metabolism of xylose and methylglucamine or higher functional diversity were found in the buffer zone, whereas a higher activity of microbes processing malic acid or fungi catabolizing added glucose under cycloheximide treatment were observed in the core zone. Geographical component obviously plays a role in the distribution of microbial activities: principal component analysis based on the rapid microtiter-plate assay (Fig. 1) revealed that the eastern and western transects are quite clearly separated along the first PCA axis, and although a similar separation can be observed also between the buffer zone and the reserve (at least in the West), microbial communities seem to be ordered along the West–East gradient rather than according to the status of the forest. Nevertheless, the first two principal axes represent only 39% of the total variation and the principal component loadings are generally low, which means that some trends or differences may have remained hidden (for eigenvalues and factor loadings, see Electronic Supporting Information files). Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.apsoil. 2013.03.005. Abundances of functional microbial groups based on the Biolog assay differed significantly between the core and the buffer zone in most cases (Table 2), always in favour of the buffer zone. Considerable is the difference in the differentiation of microbiota: whereas beta diversity in the core zone was 0.256, in the buffer zone it was only 0.050, meaning that in the old-growth forest, microbial communities contain less numerous and less evenly represented functional groups, but their compositions fluctuated more among sampling plot centres.

Redundancy analysis separated the sampling plots according to the status of forests better than the PCA in the previous case: in spite of a considerable overlap, buffer zone samples tend to be concentrated along the left side of the RDA 1 axis, whereas reservecore plots are spread predominantly at the right side of the graph (Fig. 2a). 3.2. Relationships between microbial community and the environment Characteristics of the environment used as predictor variables were of course inter-correlated (data not shown; see Electronic Supporting Information files), what complicated the identification of their causal relationships with microbial activity. For example, soil acidity and humus quality (C:N ratio), both reported to influence microbial community in other studies, were found strongly correlated (r = −0.522***). Soil chemistry proved to be strongly influenced by trees: proximity of trees, irrespective of species, generally increased organic and inorganic resource availability in the topsoil. Microbial biomass was significantly correlated with most environmental variables (Table 3). As expected, it was mainly the amount of substrates available for microbial processing (high humus, nitrogen and mineral nutrients content, surface organic matter and proximity of trees), which was indicative for high soil microbial biomass in the mineral soil. A positive relationship with increased soil acidity and C:N ratio, negative effect of tree diversity and absence of any relationships with ground vegetation were a bit surprising. For catalase activity, significant correlations were found only with soil chemistry and tree diversity. Similarly, anaerobic nitrogen mineralization rates were positively affected by humus and nutrient availability in the A-horizon and humus quality, but not the other environmental variables. Soil chemistry affected also basal respiration: generally, higher amounts of nutrients and water lead to the higher basal respiration rates. The only exception is the potassium content, where a negative correlation was found. Proximity of trees, namely noble hardwoods, and decreased diversity of trees also had a positive effect on basal respiration. The metabolic quotient showed similar associations with water and nutrient supplies, light and trees as basal respiration. Moreover, it showed significant association with the diversity and cover of understory vegetation.

E. Gömöryová et al. / Applied Soil Ecology 68 (2013) 10–19

15

Table 3 Mantel partial correlations (Bonferroni-corrected) between microbial activity indicators and environmental variables. Variable

Cat

BR

qCO2

Nmin

Chex

Stre

lSC

Fdiv

Soil chemical properties ns Moist pH −0.418*** C 0.634*** 0.598*** N C/N 0.399*** 0.121* Ca 0.130** Mg ns K

Cmic

0.357*** ns 0.422*** 0.549*** ns 0.257*** 0.241*** ns

0.290*** −0.237*** 0.500*** 0.406*** 0.406*** 0.416*** 0.414*** −0.137***

0.280*** ns ns ns ns 0.374*** 0.329*** −0.334***

0.191*** ns 0.262*** 0.417*** −0.150** 0.363*** 0.285*** 0.129*

0.154* ns 0.199*** 0.241*** ns ns ns ns

0.225*** ns ns ns ns ns ns 0.199***

ns ns −0.153* ns ns ns ns ns

−0.147** ns ns −0.160** ns ns ns −0.437***

Light and temperatures CO −0.195* −0.162* Rdiff −0.196** Rdir ns Tavg

ns ns ns ns

ns ns ns ns

0.180** ns 0.194** ns

ns ns ns ns

ns ns ns ns

ns ns ns −0.215***

ns ns ns ns

0.214*** ns 0.233*** ns

Trees IPb IPn IPf IPs IPt Trich Tdiv Litt

ns ns ns ns ns ns −0.244*** ns

ns ns ns 0.366*** 0.213*** ns −0.241*** ns

ns ns ns 0.207*** ns −0.141* ns −0.214***

ns ns ns ns ns ns ns ns

ns ns −0.207*** ns −0.187* ns −0.165* ns

ns −0.149* 0.595*** −0.306*** 0.435*** ns 0.326*** ns

ns ns 0.406*** −0.288*** 0.284*** −0.190* 0.320*** ns

ns ns ns 0.165** ns −0.195** ns ns

ns ns ns ns

ns ns ns ns

0.273*** 0.287*** 0.288*** 0.216*

ns ns ns ns

ns ns ns ns

−0.344*** −0.328*** −0.313*** ns

−0.259*** −0.278*** ns ns

0.322*** 0.293*** 0.292*** ns

0.151*** ns 0.132*** 0.265*** 0.281*** ns −0.228*** 0.457***

Ground vegetation ns Vrich ns Vdiv ns E0 ns E1

Significance labels: ns, non-significant (P > 0.05), *0.01 < P < 0.05, **0.001 < P < 0.01, ***P < 0.001.

Fungal contribution to glucose-induced respiration based on the inhibition of bacteria using streptomycin seems to be correlated with vegetation rather than soil chemistry or microclimate. Tree species exerted different effects on fungal respiration: whereas the proximity of silver fir was reflected in higher rates, presence of noble hardwoods and Norway spruce decreased respiration. However, diversity of the tree layer had a positive effect on fungal respiratory activity. Unlike fungi, bacterial contribution was significantly related to C and N content. Diversity of trees decreased respiration under cycloheximide treatment. Consequently, fungal respiration predominated over bacterial under low carbon content in the A horizon, in the proximity of beech trees but under the absence of Norway spruce, and in places with low species richness and diversity of understory plants but with high diversity of the tree layer. Substrate-induced respiration (SIR) showed some significant correlations with environmental variables depending on the added carbon substrate (Table 4). However, the relations were not as distinct as for microbial biomass or microbial activity indicators. Among the used substrates, respiration induced by phenylalanine and Tween 40 showed a clear positive reaction to higher canopy openness, radiation and average temperature; in contrast, malic acid correlated negatively with temperature. Correlations between SIR and soil chemistry characteristics were inconsistent, but some common features for different substrates could be identified: whenever C and N content correlated significantly with SIR, correlations were negative; on the other hand, better humus quality (lower C:N ratio) was usually reflected in higher respiratory activity. A higher Ca and Mg content lead to a higher celluloseinduced respiration, but the opposite was observed for xylose and partly for starch. Several substrates (malic acid, cellulose, phenylalanine, Tween 40) reacted positively to soil moisture. Quite few substrates showed a significant relationship with tree influence potential. Generally, positive correlations were observed for spruce and beech, and negative correlations for noble hardwoods

and fir. Nevertheless, a half of substrates reacted positively to treelayer diversity. High abundance and diversity of ground vegetation increased the activity of phenylalanine- and cellulose-processing functional groups, whereas correlated negatively with respiratory rates under the addition of asparagine and malic acid. Activity of most functional groups of soil microorganisms (assessed by the Biolog assay) exhibited significant correlations with some of the environmental variables (data not shown, see Electronic Supporting Information files). Again, in spite of a low consistency of correlations, some generalizations can be made. Many substrates were positively associated with a high plant cover and diversity. Out of 38 significant correlations, only 3 were negative (s2 ␤-methyl-d-glucoside, s21 glycogen). Similarly, most functional microbial groups preferred microsites with open canopy, receiving more radiation and thus enjoying higher soil temperatures (23 out of 27 significant correlations, exceptions s2 ␤-methyl-d-glucoside and s24 glycyl l-glutamic acid). Relationships with tree influence were very diverse, particular functional groups usually exhibited positive or negative affinity to particular tree species, whereby the same applies to the tree-layer diversity. Sometimes such correlations correspond to the relationships with light and temperature regime (mainly in the case of affinity to dominant tree species such as beech or spruce), in other cases, the effects seem to be species-specific. Reactions of functional groups to soil chemistry also vary. In general, activity decreases with soil moisture. High humus amount indicated by carbon and nitrogen content is usually preferred, but a negative reaction was observed in ␣d-lactose (s29). Five substrates showed a positive affinity to low humus quality, the opposite was found in two cases. Base cations (Ca, Mg) also affected the activity on different substrates, positively in 7 cases, negatively in 10 cases. A negative affinity to potassium content seems to be a general phenomenon, it was observed in 19 substrates out of 22 exhibiting significant reaction. These relationships are reflected in the associations of functional diversity with the environment (Table 3). The outcomes confirmed

16

E. Gömöryová et al. / Applied Soil Ecology 68 (2013) 10–19

Table 4 Mantel partial correlations (Bonferroni-corrected) between respiratory activity under the addition of selected substrates in the rapid microtiter-plate assay and environmental variables. Variable

Asp

Cell

Mal

Phe

Sta

Tw40

Xyl

Soil chemical properties Moist ns pH 0.189*** −0.151** C N ns −0.233*** C/N ns Ca ns Mg K ns

Arg

ns 0.209*** −0.134* ns −0.139* ns ns ns

0.267*** ns ns ns ns 0.265*** 0.291*** ns

0.321*** ns ns ns −0.187*** ns ns ns

0.191*** ns ns ns ns ns ns ns

ns ns ns ns ns ns −0.168** ns

0.286*** ns ns ns ns ns ns ns

ns ns −0.210* −0.267*** ns −0.282*** −0.212*** ns

Light and temperatures CO ns ns Rdiff Rdir ns ns Tavg

ns ns ns ns

ns ns ns ns

ns ns ns −0.289***

0.537*** 0.516*** 0.526*** 0.332***

ns ns ns ns

0.343*** 0.310*** 0.350*** 0.223*

ns ns ns ns

Trees IPb IPn IPf IPs IPt Trich Tdiv Litt

ns ns ns ns ns ns ns −0.155***

0.158* ns 0.148** −0.179*** 0.140* ns 0.186*** ns

0.145* ns ns ns ns ns ns ns

ns ns ns −0.200*** ns ns 0.289*** ns

ns ns ns ns ns ns ns ns

0.133*** ns ns ns ns ns 0.161** −0.188***

ns −0.270*** ns ns ns ns ns ns

ns ns ns ns ns ns ns ns

Ground vegetation Vrich Vdiv E0 E1

ns ns ns ns

−0.166* ns −0.181* ns

0.184* 0.189** ns ns

−0.366*** −0.351*** −0.427*** −0.274*

0.275*** 0.342*** 0.225*** ns

ns ns ns ns

ns ns ns ns

ns ns ns ns

Significance labels: ns, non-significant (P > 0.05), *0.01 < P < 0.05, **0.001 < P < 0.01, ***P < 0.001.

a positive relationship of the aboveground and belowground communities: the correlations with plant richness and diversity as well as bryophyte layer cover were positive and highly significant. Positive effects of canopy openness and light on functional diversity were also observed. On the other hand, proximity of trees, except Norway spruce, did not influence functional diversity of the microbial community, and the effect of tree richness was even negative. Among chemical properties of the soil, moisture, nitrogen and potassium content were negatively correlated with functional diversity. Redundancy analysis (RDA) as a sort of direct gradient analysis was used to assess community structure changes along the environmental gradients. The first two RDA axes accounted for 26% of variance in the species data and 34% of the species–environment relationship. The forward selection of environmental variables in the RDA yielded three variables significantly affecting frequency distributions of functional microbial groups: potassium content, ground vegetation richness and the influence potential of silver fir (Fig. 2). As indicated also by Mantel tests, most microbial groups show negative affinity to potassium content in the soil, with few exceptions (namely s24 glycyl-L-glutamic acid and s29 ␣-Dlactose). Abundance of microbes processing D-cellobiose (s25) and ␣-cyclodextrin (s17) increases with plant richness of bryophytes, in contrast to glycogen-related microorganisms (s21). The l-arginine (s4) and d-xylose (s6) functional groups seem to increase activity in the absence of fir. However, most functional groups seem to be indifferent to environmental factors which were found significant. 4. Discussion 4.1. Contrast natural vs. managed forest The inspection of variables characterizing various aspects of site conditions showed that the expectations of a higher environmental

heterogeneity and diversity of living communities in an old-growth (“primeval”) forest compared to planted or silviculturally managed stands (Lilja and Kuuluvainen, 2005; Lohmus et al., 2005; Burrascano et al., 2008) is not always true. In our case, tree species composition naturally varied much more in the reserve core (representing an old-growth forest) compared to monospecific mature spruce plantations in the buffer zone, and variation of soil chemical properties (measured by standard deviation) was also slightly higher in the reserve in some cases (base cations, C, N). However, whenever the differences in the variance of microbial activity was observed (rapid microplate assay), higher variance was found in the managed stand. Canopy in the buffer zone is generally more open, transmitting more solar radiation to the soil surface, what can be attributed to growth capacities of dominant species: spruce predominating in the buffer zone has less plastic crowns, so that gaps formed naturally or through thinnings are filled more slowly (Pretzsch and Schütze, 2005). On the other hand, although the main canopy is more broken and vertical structure is richer in the beechdominated old-growth forest, expansion of adult-beech crowns rapidly closes newly formed gaps or they are occupied by rich beech undergrowth, effectively shading the ground. More intense irradiation results in a higher plant cover and plant species richness in the buffer zone. Potentially, disturbance due to forestry operations could also have increased soil heterogeneity in the buffer zone and increase plant species richness through creating niches for new species, but none of the sampling points exhibited signs of recent soil disturbance, and the metabolic quotient qCO2 , which is a stress indicator, does not differ between the buffer zone and the reserve. However, plant communities (both ground vegetation and tree layer) are more differentiated in the old-growth than in the managed stands; in this sense, the expectation of a higher heterogeneity of natural forests was met. This pattern coincides well with the functional differentiation of decomposer communities, which is also incomparably higher in the reserve core. This is an indication

E. Gömöryová et al. / Applied Soil Ecology 68 (2013) 10–19

17

that biotic interactions may be more important for the composition of microbiota than abiotic environment. 4.2. Relationships between aboveground and belowground diversity Microbial community is a complex system which can be characterized by many descriptors associated with different aspects of its functioning (Stoyan et al., 2000). Therefore, straightforward responses to the changes of belowground environment and aboveground plant communities can hardly be expected because of versatile feedbacks among all three systems (Naeem and Li, 1997). The relationship between the sources of litter and decomposer communities has been abundantly documented: plant species differ in the taxonomical composition of the associated soil bacteria and fungi (Carson et al., 2010; Eisenhauer et al., 2010; Millard and Singh, 2010), and even the host plant genotype may co-determine soil microbial community composition (Schweitzer et al., 2008; Maul and Drinkwater, 2010; Thebault et al., 2010). However, high plant diversity is not always reflected in the species (or highertaxon) diversity of the belowground community (Kielak et al., 2008; Dimitriu and Grayston, 2010). The reasons for an absence of such link may differ: in studies based on manipulative experiments, the observation period may be too short for the microbial community to reach the “climax” stage, whereas in observational studies in natural ecosystems, occurrence of particular plant species may play a role, as there may exist contrasting effects of plants on the diversity of the associated microbiota among particular species and even among individuals (Maul and Drinkwater, 2010). Our study focused on functional rather than taxonomical diversity of microbiota. We regarded the composition of functional groups as more ecologically relevant than the representation of species or higher taxa. In microbial communities, the same biochemical function may be executed by different taxa and vice versa, a single taxon may contain functionally specialized lines (Baldrian, 2009). Unicellular microorganisms are characterized by extremely short generation times and extremely big population sizes (compared to multicellular eukaryotes), so that beneficial mutations (such as acquisition of a new function) can rapidly spread in their populations (Silver and Phung, 1996), even across taxon boundaries thanks to horizontal gene transfer (in prokaryotes). Composition of microbial community may thus be a matter of selection of functionally appropriate genotypes related to substrate quality, irrespective of taxa. Reports about the relationship of plant diversity and functional diversity or activity of soil microbiota are controversial: positive effects were observed by Eisenhauer et al. (2010) and Laughlin et al. (2010), but no or even negative effects were found by Hedlund et al. (2003) or Keith et al. (2008). Our data do not allow an unambiguous interpretation of the observed relationships between plant and microbial communities either, as the effects of trees and understory plants seem to diverge. A positive correlation between ground vegetation species richness and diversity and microbial functional diversity was confirmed, but there are no signs of enhanced microbial activity under high understory plant diversity, as both positive and negative effects on the activity of functional microbial groups were observed. The tree layer seems to affect microbiota just in the opposite way. High tree species richness decreased functional diversity. Diversity of the tree layer enhances the activity of specific microbial groups, but decreases microbial biomass and basal respiration as summary descriptors of the state of microbial community. As shown by Eisenhauer et al. (2010), plant species are unique in their effects on the belowground system. Interactions among the effects of trees as major sources of substrates for microbiota may thus eventually result in hampering microbial growth and activity. However, this hypothesis requires explicit testing.

Fig. 2. Redundancy analysis of soil microbial data based on the Biolog assay: (a) sample positions, and (b) functional groups positions and significant environmental variables which passed the forward selection. Arrow tips show the positions of functional microbial groups (metabolizing specific substrates): s2, ␤-methyl-dglucoside; s3, d-galactonic acid ␥-lactone; s4, l-arginine; s5, pyruvic acid methyl ester; s6, d-xylose; s7, d-galacturonic acid; s8, l-asparagine; s9, Tween 40; s10, ierythritol; s11, 2-hydroxybenzoic acid; s12, l-phenylalanine; s13, Tween 80; s14, d-mannitol; s15, 4-hydroxybenzoic acid; s16, l-serine; s17, ␣-cyclodextrin; s18, N-acetyl-d-glucosamine; s19, ␥-hydroxybutyric acid; s20, l-threonine; s21, glycogen; s22, d-glucosaminic acid; s23, itaconic acid; s24, glycyl-l-glutamic acid; s25, d-cellobiose; s26, glucose-1-phosphate; s27, ␣-ketobutyric acid; s28, phenylethylamine; s29, ␣-d-lactose; s30, dl-␣-glycerol phosphate; s31, d-malic acid; s32, putrescine.

Interestingly, fungal respiration increased under high tree diversity but decreased under high ground-layer diversity. This is partly in accord with the observations of Thoms et al. (2010), but whether this relationship is causally associated with different litter

18

E. Gömöryová et al. / Applied Soil Ecology 68 (2013) 10–19

composition (e.g., higher lignification or content of recalcitrant substances such as tannins in tree litter) remains questionable. 4.3. Microbial community responses to the environment In contrast to a previous study done with almost identical methodology (Gömöryová et al., 2009), the present study failed to find clear and consistent relationships between soil microbial community characteristics and plant communities. This may be related to a more complicated ecological situation: in the former case, a relatively uniform grassland colonized by a single tree species resulted in broad but inter-correlated ecological gradients inducing consistent responses of microbiota, whereas in the latter case, a heterogeneous forest consisting of parts with different tree-layer structures was analyzed. Several relationships between microbial activities and the environment, found by Gömöryová et al. (2009) and regarded by them as generally valid (namely microbial activity responses to light and temperature regimes) might have in fact been mediated by other environmental factors, including those not considered in the study. However, a positive effect of ground vegetation richness and diversity on the functional diversity of microbiota was confirmed. The present study confirmed the expected positive effects of substrate availability (litter, proximity of trees) on microbial biomass and contrasting effects of different tree species on microbial activity, demonstrated earlier by Priha et al. (1999), Merilä et al. (2010) or Fekete et al. (2011). Nevertheless, it showed that they cannot be simplified to conifers vs. broadleaves contrast, as proposed by White et al. (2005). In contrast to Antisari et al. (2011), we observed similarity of responses to the proximity of beech and spruce on one side, and noble hardwoods and fir on the other side. This is not surprising for beech and noble hardwoods, strongly differing in the chemistry and biological quality of litter (Thoms et al., 2010), but unexpected in the case of silver fir. Chemical properties of soil strongly influenced most microbial activity indicators. As expected, amounts of organic and inorganic resources in the topsoil were positively reflected by the decomposer community. Increased soil acidity and lower humus quality commonly hamper microbial activity (Smolander et al., 2005; Högberg et al., 2007; Thoms et al., 2010). Surprisingly, we observed the opposite. However, Aikio et al. (2000) observed a coincidence of C-to-N and respiration activity trends in a successional series, and explained it by balance between fixation of organic carbon in microbial biomass and its utilization as energy source metabolic processes. Alternatively, the decrease of C-to-N ratio in soil organic matter is associated with gradual transformation of humus from low- to high-molecular-weight compounds, making it less accessible for microbial decomposition. 5. Conclusions Horizontal and vertical structure of the tree layer largely affects not only biotic components of a forest ecosystem, but also spatial distribution of microsites. Therefore, separating the effects of microclimate, soil properties and understory plants on soil microbiota from the effects of trees is quite difficult, as forests are by far not ideal objects for manipulative experiments. Moreover, the dynamics of soil microbial communities is partly driven by intrinsic factors. However, the presented results demonstrated mutual links between the aboveground and belowground components of a forest ecosystem. Microclimate seems to affect soil microbiota less than interactions with the vegetation. Microbial activity was shown to be strongly affected by availability of organic and mineral resources in the topsoil itself, but spatial distribution of resources was linked with tree distribution. As judged from microbial activity

and functional diversity, soil microbiota prefers diverse groundlayer vegetation, but not necessarily diverse tree vegetation. A comparison with previous studies based on similar methodology (Gömöryová et al., 2006, 2009) revealed several discrepancies, reminding that plant–soil relationships may vary depending on the community and ecosystem context. Acknowledgements The study was supported by research grants of the Slovak Research and Development Agency no. APVV-0580-10. The participation of D. Gömöry and K. Ujházy resulted from the implementation of the project Centre of Excellence “Adaptive Forest Ecosystems”, ITMS: 26220120006, supported by the Research and Development Operational Programme funded by the ERDF (30%). Technical assistance of B. Benˇcat’ová, P. Glonˇcák, E. Kriˇzová and J. Niˇc is heartily appreciated. References Aikio, S., Väre, H., Strömmer, R., 2000. Soil microbial activity and biomass in the primary succession of a dry heath forest. Soil Biol. Biochem. 32, 1091–1100. Antisari, L.V., Marinari, S., Dell’Abate, M.T., Baffi, C., Vianello, G., 2011. Plant cover and epipedon SOM stability as factors affecting brown soil profile development and microbial activity. Geoderma 161, 212–224. Baldrian, P., 2009. Microbial enzyme-catalyzed processes in soils and their analysis. Plant Soil Environ. 55, 370–378. Bardgett, R.D., Bowman, W.D., Kaufmann, R., Schmidt, S.K., 2005. A temporal approach to linking aboveground and belowground ecology. Trends Ecol. Evol. 20, 634–641. Bever, J.D., 2003. Soil community dynamics and the coexistence of competitors: conceptual frameworks and empirical tests. New Phytol. 157, 465–473. Bobiec, A., 1998. The mosaic diversity of field layer vegetation in the natural and ˙ Plant Ecol. 136, 175–187. exploited forests of Białowieza. Bublinec, E., Pichler, V., 2001. Slovenské pralesy, diverzita a ochrana. ÚEL SAV, Zvolen. Burrascano, S., Lombardi, F., Marchetti, M., 2008. Old-growth forest structure and deadwood: are they indicator of plant species composition? A case study from central Italy. Plant Biosyst. 142, 313–323. Campbell, C.D., Chapman, S.J., Cameron, C.M., Davidson, M.S., Potts, J.M., 2003. A rapid microtiter plate method to measure carbon dioxide evolved from carbon substrate amendments so as to determine the physiological profiles of soil microbial communities by using whole soil. Appl. Environ. Microbiol. 69, 3593–3599. Carson, J.K., Gleeson, D.B., Clipson, N., Murphy, D.V., 2010. Afforestation alters community structure of soil fungi. Fungal Biol. 114, 580–584. Chen, J., Saunders, S.C., Crow, T.R., Naiman, R.J., Brosofske, K.D., Mroz, G.D., Brookshire, B.L., Franklin, J.F., 1999. Microclimate in forest ecosystem and landscape ecology. Bioscience 49, 288–297. Dimitriu, P.A., Grayston, S.J., 2010. Relationship between soil properties and patterns of bacterial beta-diversity across reclaimed and natural boreal forest soils. Microb. Ecol. 59, 563–573. Ehrenfeld, J.G., Ravit, B., Elgersma, K., 2005. Feedback in the plant–soil system. Annu. Rev. Environ. Resour. 30, 75–115. Eisenhauer, N., Bessler, H., Engels, C., Gleixner, G., Habekost, M., Milcu, A., Partsch, S., Sabais, A.C.W., Scherber, C., Steinbeiss, S., Weigelt, A., Weisser, W.W., Scheu, S., 2010. Plant diversity effects on soil microorganisms support the singular hypothesis. Ecology 91, 485–496. Fekete, I., Varga, C., Kotroczo, Z., Tóth, J.A., Varbiro, G., 2011. The relation between various detritus inputs and soil enzyme activities in a Central European deciduous forest. Geoderma 167–168, 15–21. Ferris-Kaan, R., Peace, A.J., Humphrey, J.W., 1998. Assessing structural diversity in managed forests. Forest Sci. 51, 331–342. Franklin, J.F., Spies, T.A., Van Pelt, R., Carey, A.B., Thornburgh, D.A., Berg, D.R., Lindenmayer, D.B., Harmon, M.E., Keeton, W.S., Shaw, D.C., Bible, K., Chen, J.Q., 2002. Disturbances and structural development of natural forest ecosystems with silvicultural implications, using Douglas-fir forests as an example. For. Ecol. Manage. 155, 399–423. Frazer, G.W., Canham, C.D., Lertzman, K.P., 1999. Gap Light Analyzer (GLA): Imaging Software to Extract Canopy Structure and Gap Light Transmission Indices from True-colour Fisheye Photographs. Users Manual and Program Documentation. Simon Fraser University/Institute of Ecosystem Studies, Burnaby/Millbrook. Gibson, D.J., 1988. The maintenance of plant and soil heterogeneity in dune grassland. J. Ecol. 76, 497–508. Gömöryová, E., Gregor, J., Pichler, V., Gömöry, D., 2006. Spatial patterns of soil microbial characteristics and soil moisture in a natural beech forest. Biologia (Bratislava) 61, 329–333. Gömöryová, E., Hrivnák, R., Janiˇsová, M., Ujházy, K., Gömöry, D., 2009. Changes of the functional diversity of soil microbial community during the colonization of abandoned grassland by a forest. Appl. Soil Ecol. 43, 191–199.

E. Gömöryová et al. / Applied Soil Ecology 68 (2013) 10–19 Hedlund, K., Santa-Regina, I., van der Putten, W.H., Lepˇs, J., Diaz, T., Korthals, G.W., Lavorel, S., Brown, V.K., Gormsen, D., Mortimer, S.R., Barrueco, C.R., Roy, J., ˇ ˇ P., Smilauerová, M., van Dijk, C., 2003. Plant species diversity, plant Smilauer, biomass and responses of the soil community on abandoned land across Europe: idiosyncracy or above-belowground time lags. Oikos 103, 45–58. Hill, M.O., 1973. Diversity and evenness: a unifying notation and its consequences. Ecology 54, 427–432. Högberg, M.N., Chen, Y., Högberg, P., 2007. Gross nitrogen mineralization and fungito-bacteria ratios are negatively correlated in boreal forests. Biol. Fertil. Soils 44, 363–366. IFER, 2008. FieldMap – tool designed for computer aided field data collection. IFER ´ ˚ s.r.o. http://www.fieldmap.cz/ – Ústav pro vyzkum lesních ekosystému, Insam, H., 1997. A new set of substrates proposed for community characterization in environmental samples. In: Insam, H., Rangger, A. (Eds.), Microbial Communities. Functional versus Structural Approaches. Springer, Heidelberg, pp. 260–261. Islam, K.R., Weil, R.R., 1998. Microwave irradiation of soil for routine measurement of microbial biomass carbon. Biol. Fertil. Soils 27, 408–416. Jost, L., 2007. Partioning diversity into independent alpha and beta components. Ecology 88, 2427–2439. Kandeler, E., 1993. Bestimmung der N-mineralisation im anaeroben Brutversuch. In: Schinner, F., Öhlinger, R., Kandeler, E., Margesin, E.R. (Eds.), Bodenbiologische Arbeitsmethoden. Springer, Heidelberg, pp. 160–161. Keith, A.M., van der Wal, R., Brooker, R.E., Osler, G.H.R., Chapman, S.J., Burslem, D.F.R.P., Elston, D.A., 2008. Increasing litter species richness reduces variability in a terrestrial decomposer system. Ecology 89, 2657–2664. Khaziev, F.Kh., 1976. Fermentativnaya aktivnost’ pochv. Metodicheskoe Posob‘e, Moskva. Kielak, A., Pijl, A.S., van Veen, J.A., Kowalchuk, G.A., 2008. Differences in vegetation composition and plant species identity lead to only minor changes in soil borne microbial communities in a former arable field. FEMS Microbiol. Ecol. 63, 372–382. ˇ 1995. Die Urwälder der Westkarpaten. Gustav Fischer Verlag, Jena. Korpel’, S., Kuuluvainen, T., Pukkala, T., 1989. Effect of Scots pine seed trees on the density of ground vegetation and tree seedlings. Silva Fenn. 23, 159–167. Laughlin, D.C., Hart, S.C., Kaye, J.P., Moore, M.M., 2010. Evidence for indirect effects of plant diversity and composition on net nitrification. Plant Soil 330, 435–445. Lilja, S., Kuuluvainen, T., 2005. Structure of old Pinus sylvestris dominated forest stands along a geographic and human impact gradient in mid-boreal Fennoscandia. Silva Fenn. 39, 407–428. Lohmus, A., Lohmus, P., Remm, J., Vellak, K., 2005. Old-growth structural elements in a strict reserve and commercial forest landscape in Estonia. For. Ecol. Manage. 216, 201–215. Mahaming, A.R., Mills, A.A.S., Adl, S.M., 2009. Soil community changes during secondary succession to naturalized grasslands. Appl. Soil. Ecol. 41, 137–147. Matthews, J.D., 1989. Silvicultural Systems. Oxford Science Publications, Oxford. Maul, J., Drinkwater, L., 2010. Short-term plant species impact on microbial community structure in soils with long-term agricultural history. Plant Soil 330, 369–382. Merilä, P., Malmivaara-Lamsa, M., Spetz, P., Stark, S., Vierikko, K., Derome, J., Fritze, H., 2010. Soil organic matter quality as a link between microbial community structure and vegetation composition along a successional gradient in a boreal forest. Appl. Soil Ecol. 46, 259–267. Miki, T., Ushio, M., Fukui, S., Kondoh, M., 2010. Functional diversity of microbial decomposers facilitates plant coexistence in a plant-microbe-soil feedback model. Proc. Natl. Acad. Sci. U.S.A. 107, 14251–14256. Millard, P., Singh, B.K., 2010. Does grassland vegetation drive soil microbial diversity? Nutr. Cycl. Agroecosyst. 88, 147–158. Naeem, S., Li, S., 1997. Biodiversity enhances ecosystem reliability. Nature 390, 507–509. Otto, H.-J., 1994. Waldökologie. Ulmer, Stuttgart. Paillet, Y., Bergès, L., Hjältén, J., Ódor, P., Avon, C., Bernhardt-Römermann, M., Bijlsma, R.J., De Bruyn, L., Fuhr, M., Grandin, U., Kanka, R., Lundin, L., Luque, S., Magura, T., Matesanz, S., Mészáros, I., Sebastià, M.T., Schmidt, W., Standovár, T., Tóthmerész, B., Uotila, A., Valladares, F., Vellak, K., Virtanen, R., 2010. Biodiversity differences between managed and unmanaged forests: meta-analysis of species richness in Europe. Conserv. Biol. 24, 101–112.

19

Pärtel, M., Wilson, S.C., 2002. Root dynamics and spatial pattern in prairie and forest. Ecology 83, 1199–1203. Pennanen, T., Liski, J., Bååth, E., Kitunen, V., Uotila, J., Westman, C.H., Fritze, H., 1999. Structure of the microbial communities in coniferous forest soils in relation to site fertility and stand development stage. Microb. Ecol. 38, 168–179. Pregitzer, C.C., Bailey, J.K., Hart, S.C., 2010. Soils as agents of selection: feedbacks between plants and soils alter seedling survival and performance. Evol. Ecol. 24, 1045–1059. Pretzsch, H., Schütze, G., 2005. Crown allometry and growing space efficiency of Norway spruce (Picea abies [L.] Karst.) and European beech (Fagus sylvatica L.) in pure and mixed stands. Plant Biol. 7, 628–639. Priha, O., Hallantie, T., Smolander, A., 1999. Comparing microbial biomass, denitrification enzyme activity, and numbers of nitrifiers in the rhizospheres of Pinus sylvestris, Picea abies and Betula pendula seedlings by microscale methods. Biol. Fertil. Soils 30, 14–19. Quinn, G.P., Keough, M.J., 2002. Experimental Design and Data Analysis for Biologists. Cambridge University Press, Cambridge. Saetre, P., Bååth, E., 2000. Spatial variation and patterns of soil microbial community structure in a mixed spruce-birch stand. Soil Biol. Biochem. 32, 909–917. SAS, 2009. SAS/STAT® User’s Guide. http://support.sas.com/documentation/ onlinedoc/stat/index.html Schweitzer, J.A., Bailey, J.K., Fischer, D.G., Leroy, C.J., Lonsdorf, E.V., Whitham, T.G., Hart, S.C., 2008. Plant–soil–microorganism interactions: heritable relationship between plant genotype and associated soil microorganisms. Ecology 89, 773–781. Silver, S., Phung, L.T., 1996. Bacterial heavy metal resistance: new surprises. Annu. Rev. Microbiol. 50, 753–789. Smolander, A., Loponen, J., Suominen, K., Kitunen, V., 2005. Organic matter characteristics and C and N transformations in the humus layer under two tree species, Betula pendula and Picea abies. Soil Biol. Biochem. 37, 1309–1318. Smouse, P.E., Long, J.C., Sokal, R.R., 1986. Multiple regression and correlation extensions of the Mantel test of matrix correspondence. Syst. Zool. 35, 727–732. Stoyan, H., De-Polli, H., Böhm, S., Robertson, G.P., Paul, E.A., 2000. Spatial heterogeneity of soil respiration and related properties at the plant scale. Plant Soil 222, 203–214. Sullivan, T.P., Sullivan, D.S., Lindgren, P.M.F., Ransome, D.B., 2009. Stand structure and the abundance and diversity of plants and small mammals in natural and intensively managed forests. For. Ecol. Manage. 258, 127–141. ˇ ter Braak, C.J.F., Smilauer, P., 2002. Reference Manual and User’s Guide to Canoco for Windows. Software for Canonical Community Ordination (version 4). Centre of Biometry, Wageningen. Thebault, A., Frey, B., Mitchell, E.A.D., Buttler, A., 2010. Species-specific effects of polyploidisation and plant traits of Centaurea maculosa and Senecio inaequidens on rhizosphere microorganisms. Oecologia 163, 1011–1020. Thoms, C., Gattinger, A., Jacob, M., Thomas, F.M., Gleixner, G., 2010. Direct and indirect effects of tree diversity drive soil microbial diversity in temperate deciduous forest. Soil Biol. Biochem. 42, 1558–1565. Ujházy, K., Kriˇzová, E., Glonˇcák, P., Benˇcat’ová, B., Niˇc, J., 2013. Tree species and management effect on herb layer species composition in mountain fir-beech forests of the Western Carpathians. In: Kozak, J., Ostapowicz, K., ˙ B. (Eds.), The Carpathians: Integrating Nature and Bytnerowicz, A., Wyzga, Society Towards Sustainability. Springer, Berlin-Heidelberg, http://dx.doi.org/ 10.1007/978-3-642-12725-0 18. Wardle, D.A., Bardgett, R.D., Klironomos, J.N., 2004. Ecological linkages between aboveground and belowground biota. Science 304, 1629–1633. White, C., Tardif, J.C., Adkins, A., Staniforth, R., 2005. Functional diversity of microbial communities in the mixed boreal plain forest of central Canada. Soil Biol. Biochem. 37, 1359–1372. Wilkinson, S.C., Anderson, J.M., 2001. Spatial patterns of soil microbial communities in a Norway spruce (Picea abies) plantation. Microb. Ecol. 42, 248–255. Zak, D.R., Holmes, W.E., White, D.C., Peacock, A.D., Tilman, D., 2003. Plant diversity, soil microbial communities, and ecosystem function: are there any links? Ecology 84, 2042–2050.