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Soil Biology & Biochemistry 38 (2006) 1745–1756

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Microbial activity and community structure of a soil after heavy metal contamination in a model forest ecosystem

Beat Freya, , Michael Stemmerb, Franco Widmerc, Joerg Lustera, Christoph Sperisena

aSoil Ecology, Swiss Federal Research Institute WSL, CH-8903 Birmensdorf, Switzerland bUniversity of Agricultural Sciences Vienna (BOKU), A-1180 Vienna, Austria cMolecular

Ecology, Agroscope FAL Reckenholz, CH-8046 Zu¨ rich, Switzerland

Received 23 June 2005; received in revised form 14 October 2005; accepted 14 November 2005Available online 20 February 2006

Abstract

We assessed the effects of chronic heavy metal (HM) contamination on soil microbial communities in a newly established forest ecosystem. We hypothesized that HM would affect community function and alter the microbial community structure over time and that the effects are more pronounced in combination with acid rain (AR). These hypotheses were tested in a model forest ecosystem consisting of several tree species (Norway spruce, birch, willow, and poplar) maintained in open top chambers. HMs were added to the topsoil as filter dust from a secondary metal smelter and two types of irrigation water acidity (ambient rain vs. acidified rain) were applied during four vegetation periods. HM contamination strongly impacted the microbial biomass (measured with both fumigation–extraction and quantitative lipid biomarker analyses) and community function (measured as basal respiration and soil hydrolase activities) of the soil microbial communities. The most drastic effect was found in the combined treatment of HM and AR, although soil pH and bioavailable HM contents were comparable to those of treatments with HM alone. Analyses of phospholipid fatty acids (PLFAs) and terminal restriction fragment length polymorphisms (T-RFLPs) of PCR-amplified 16S ribosomal DNA showed that HM treatment affected the structure of bacterial communities during the 4-year experimental period. Very likely, this is due to the still large bioavailable HM contents in the HM contaminated topsoils at the end of the experiment.r 2006 Elsevier Ltd. All rights reserved.

Keywords: Heavy metals; Acid rain; Model forest ecosystems; Soil microbial communities; PLFA profiles; T-RFLP; Genetic fingerprinting; 16S rRNAgene

1. Introduction

Microbial communities play important roles in soil because of the many functions they perform in nutrient cycling, plant symbioses, decomposition, and other ecosystem processes (Nannipieri et al., 2003). Large heavy metal (HM) contents in soil are of concern because of their toxicity to soil microorganisms and impairment of ecosystem functions (Giller et al., 1998). Short-term responses of microbial communities to HM contamination are well known (Shi et al., 2002; Ranjard et al., 2000; Gremion et al., 2004; Rajapaksha et al., 2004) but medium- and long- term effects of HM in the field have been less frequently

Corresponding author. Tel.: +41 1 739 25 41; fax: +41 1 739 22 15.E-mail address: [email protected] (B. Frey).

investigated (Pennanen et al., 1996; Kandeler et al., 2000; Sandaa et al., 2001; Renella et al., 2004). Most of these studies reported reduced soil microbial activities and microbial biomass, inhibition of organic matter mineraliza- tion and changes in microbial community structure following application of HMs to soil. Since HM cannot be degraded they accumulate in the upper soil layer. The hazard posed by HM in soil is suggested to be a function of their relative mobility and bioavailability, which are dependent on soil characterisitics such as pH, mineralogy, texture, and organic matter content as well as on the source and quantities of HM in the soil (Lofts et al., 2004).

While analytical methods have been developed for estimating the bioavailability of HMs in soil (Sauve et al., 1998; Lofts et al., 2004) the relationship of these values to ecological toxicity is not fully understood.

0038-0717/$ - see front matter r 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.soilbio.2005.11.032

1 B. Frey et al. / Soil Biology & Biochemistry 38 (2006) 1745–B. Frey et al. / Soil Biology & Biochemistry 38 (2006) 1745– 1

Sand (%) Silt (%) Clay (%) pH CaCl2 ÞðaCEC (mmolc 1 b 1 c

Topsoil 36 49 15 6.6 102 99.9 15.1 1.5Subsoil 87 8 5 4.2 31 35.9 3.2 o0.3

Therefore, indicators of the ecological harm caused by HM pollutants will be the indigenous soil organisms. Of these, the microbial communities are the most obvious group to study as they are ubiquitous, respond rapidly to changing conditions (Nannipieri et al., 2003) and it has been suggested that they should be included in ecological risk assessments as important endpoints to follow the toxicity with time (White et al., 1998). Therefore, an overall assessment including the combined use of various tests at the community functional and structural level is needed in order to detect any potential hazard of the pollutant in the soil with time (Harris, 2003; Keller and Hammer, 2004).

The present study is part of a larger research project aiming to investigate the HM and water fluxes in model ecosystem chambers and to trace and better understand the reactions of plants and associated organisms to the chronic influence of important soil pollutants and rain acidity (Menon et al., 2005). Natural conditions comprise the occurrence of more than one HM in the soil as well as the existence of a plant community growing together in competition for light, nutrients and space. The experimental design of the project modelled this fact with the establishment of different tree species growing together in model ecosystems on moderately contaminated topsoil with HM dust. At present, we have very little knowledge on whether juvenile forest vegetation on a HM-contaminated soil leads to a reduced risk/toxicity for soil microorganisms. Knowledge of the microbial community function and structure represents a first step toward understanding soil function in response to the HM pollution. We hypothesized that chronic exposure of HM would affect community function and alter the microbial community structure over time and that the effects are more pronounced when combined with acid rain (AR) because the solubility of most HMs in soil tends to increase with decreasing soil pH. In three successive years, bioavailable HM contents in the soil were monitored using HM-specific recombinant bacterial sensors (Corbisier et al., 1999). Community function analysis was carried out by heterotrophic respiration and soil enzymatic activities (Zimmermann and Frey,2002). The changes in the microbial community structures were determined by two fingerprinting techniques: polymerase chain reaction (PCR)–terminal restriction fragment length polymorphisms (T-RFLPs) of total eubacterial 16S ribosomal DNA (Liu et al., 1997; Tom-Petersen et al.,2003; Pesaro et al., 2004; Hartmann et al., 2005) and

analysis of phospholipid fatty acids (PLFAs; Bundy et al.,2004; Tscherko et al., 2004).

2. Materials and methods

2.1. Experimental system

The experiments were performed in the Open Top Chamber (OTC) facility of the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) at Birmens- dorf, Switzerland. Below ground each OTC contained two lysimeters each of 3 m2 surface area packed with a subsoil (depth 15–95 cm). The pH of the topsoil (0–15 cm) was 6.6 and of the subsoil was pH 4.2. Details of the OTCs and their lysimeters were described in Menon et al. (2005) and the properties of the soils are given in Table 1. Model ecosystems were established with several tree species (Picea abies (L.) Karst., Betula pendula, Populus tremula, and Salix viminalis) in 16 OTCs. Each model ecosystem consisted of 6 Norway spruce, 4 poplar, 2 willow and 2 birch seedlings and was planted in the spring of 2000. The trees were either 4 year old seedlings (spruce, birch), or stem cuttings from 4 year old plants (poplar, willow). Each of the four combinations of topsoil treatment (with/without HM dust) and irrigation water acidity (ambient/ acidified) was replicated in four chambers using a Latin Square design. The following abbreviations are used to denote treatments: HM, heavy metal contaminated topsoil (and ambient rain); AR, acid rain (with no metal contamination); HMAR, combination of heavy metal- contaminated topsoil and acid rain; CO, control (no contamination, ambient rain). HMs were applied to the OTCs as filter dust obtained from a secondary metal smelter (Swissmetal, Dornach, Switzerland) and mixed with the 15 cm top soil layer at the beginning of the experiment in spring 2000. HNO3-extractable contentsaveraged 740 mg kg 1 Cu, 3000 mg kg 1 Zn, 22 mg kg 1

Cdand 110 mg kg 1 Pb in the contaminated topsoil after mixing ðn ¼ 8Þ. The background contents of the unconta-minated topsoil were 21 mg kg 1 Cu, 79 mg kg 1

Zn,o 1 mg kg 1 Cd and 23 mg kg 1 Pb. During the vegetation period (May – October inclusive), the roofs of the OTC closed automatically during rainfall events to exclude natural precipitation and the chambers received irrigation with synthetic ‘neutral’ rain (pH 5.5) or AR (pH 3.5). The ionic composition of the synthetic rain was similar to that of local natural rain and the pH was adjusted with HCl

Table 1Selected physical and chemical properties of the soil materials used at the beginning of the experiment (2000)

1kg ) Base saturation (%) Corg (g kg ) Ntot (g kg )

aCEC ¼ cation exchange capacity.borg ¼ organic.

1 B. Frey et al. / Soil Biology & Biochemistry 38 (2006) 1745–B. Frey et al. / Soil Biology & Biochemistry 38 (2006) 1745– 1ctot ¼ total.


(Menon et al., 2005). During winter (November – April) the roofs were open continuously and the lysimeters received natural precipitation.

2.2. Sampling

Soil samples were collected from all 16 OTCs using a soil corer (4-cm diameter, 8 cores from each chamber) from the top 15 cm in October 2000 and then in August in each of the three subsequent years (2001, 2002 and 2003). The sampling design for these eight soil cores was similar for each chamber and harvest year. Since different tree roots can favour different microbial communities in the soil, our samples were taken in the vicinity of Norway spruce, poplar, willow and beech plants (two per plant species). The eight soil cores of each OTC were pooled and sieved (2-mm mesh). A portion of the soil samples was used immediately for determination of basal respiration and microbial biomass C and the remainder was frozen either in liquid nitrogen for DNA extraction or at 20 1C for PLFA analysis, enzyme assays and bioavailable HM contents. The remaining soil was dried (105 1C) for the determination of soil dry weights. Samples for enzyme assays and PLFA analysis were taken in 2003 only. For the measurements of soil pH, the first sampling of the topsoils was performed in May 2000, i.e. just before trees were planted, then in autumn 2000 and in three successive years. For each OTC, three samples were taken with a cylindrical sampler (diameter 5 cm; length: 15 cm) and were pooled for analysis. A visual inspection of all sampling cores taken revealed that topsoil and subsoil were clearly distinguish- able and were not mixed. All samples were dried at 40 1C and sieved to 2 mm.

2.3. Soil pH and bioavailable heavy metals contents

Soil pH was measured potentiometrically in 0.01 M CaCl2 with a soil:extractant ratio of 1:2. Bioavailable metal concentrations were measured with metal-specific bacterial biosensors (BIOMET): the strains AE1235 (Cupriavidus necator (formerly Alcaligenes eutrophus)), AE1239, AE1433 and AE2450 were used to determine Cd, Cu, Pb and Zn, respectively (Corbisier et al., 1999). The analyses were performed at the Flemish Institute for Technological Research (VITO, Belgium) where the biosensors were developed.

2.4. Basal soil respiration

Water contents of the soil samples were adjusted to two- thirds of their water-holding capacity before determination of respiration. Basal respiration (CO2 evolution without added substrate) was determined by incubating 20-g (oven- dry basis) aliquots of moist soil samples for 3 days in gas- tight vessels (Zimmermann and Frey, 2002). The CO2

evolved was absorbed in 20 ml 0.025 M NaOH solution and determined by titration of the excess NaOH with 0.025 M

HCl. Basal respiration was determined in triplicate and is reported on a dry weight basis.

2.5. Soil hydrolase activities

Hydrolase activities (phosphatase, b-glucosidase, N-acetyl-b-glucosaminidase, b-glucuronidase and leucin- aminopeptidase) were measured simultaneousely using a multiple substrate enzyme assay described in detail by Stemmer (2004). Briefly, methylumbelliferone (MU)- and methylcumarinylamid (MCA)-derivatives were used as hydrolase substrates and applied simultaneously to 500 mg fresh soil in a buffered solution (2 ml) at the following concentrations: MU-phosphate 5000 mM, MU-b-glucoside 1000 mM, MU-N-acetyl-b-glucosaminide500 mM, MU-b-glucuronide 250 mM and MCA-leucine1500 mM. The incubation was done at pH 6.5 (soil pH) and 30 1C for 1, 2, 3 and 4 h. After incubation, samples were treated with a methanol/phosphate buffer mixture, shaken, centrifuged and filtered. Separation and quantification of remaining MU- and MCA-derivatives and of liberated MU and MCA was done by gradient HPLC. Hydrolase activities were calculated from substrate deple- tion over time. They were determined in triplicate samples and are reported on a dry weight basis.

2.6. Soil microbial biomass

Microbial biomass C (Cmic) was determined by the chloroform fumigation–extraction method (Vance et al.,1987) with field-moist samples (equivalent to 20 g dry weight). The filtered soil extracts of both fumigated and unfumigated samples were analysed for soluble organic C using a TOC-5000 total organic C analyser (Shimadzu, Kyoto, Japan). Cmic was estimated on the basis of the difference between the organic C extracted from the fumigated soil and that from the unfumigated soil. Wu et al. (1990) suggested a factor of 2.22 for the extraction efficiency of the method and this value was used to convert all total carbon results for the extracts to biomass carbon in the soil samples.

2.7. Phospholipid fatty acid (PLFA) analysis

The lipid extraction, fractionation, mild alkaline methanolysis and GC analysis were accomplished according to Frostegard et al. (1993b). Briefly, lipids were extracted from 1.5 g fresh soil using a one-phase mixture of chloroform/methanol/citrate buffer. Polar lipids were separated using silicic acid columns followed by a mild alkaline methanolysis to form fatty acid methyl esters for GC analysis. The total amount of PLFAs included all detected38 PLFAs and was used to indicate the total microbialbiomass. The fatty acids i15:0, a15:0, i16:0, i17:0, a17:0,10Me17:0, 16:1o7, 16:1o5, cy17:0, 18:1o7, cy19:0 and10Me18:0 were chosen as an index of bacterial biomass(Frostegard et al., 1993a, b). Gram-positive bacteria were


identified by the PLFAs: i15:0, a15:0, i16:0, i17:0, a17:0 and10Me17:0 and Gram-negative bacteria were represented by the PLFAs: 16:1o7, 16:1o5, cy17:0, 18:1o7 and cy19:0 (O’Leary and Wilkinson, 1988; Frostegard et al., 1993a, b). The quantity of the fatty acid 18:2o6,9 was used as an indicator of fungal biomass since it is suggested to be mainly of fungal origin in soil (Olsson, 1999). Based on the findings by Kroppenstedt (1985), the fatty acid 10Me18:0 was used to indicate actinomycete biomass.

2.8. DNA extraction and 16S rDNA PCR amplification

Soil samples for DNA analysis were collected in three consecutive years (2001, 2002 and 2003) and total DNA was extracted according to the protocol of Burgmann et al. (2001). Briefly, 0.5 g fresh soil and 0.5 g glass beads (0.1 mm diameter) were suspended in an extraction buffer (0.2 M Na3PO4 [pH 8], 0.1 M NaCl, 50 mM EDTA, 0.2% CTAB) and extracted three times with each 1 ml extraction buffer with a bead beating procedure in a FastPrep bead beater (FP 120, Savant Instruments) at 5.5 m s 1 and for 45 s. DNA was purified by chloroform extraction with 2 ml chloroform/isoamyl alcohol (proportion 24:1) and precipi- tated by addition of 3 ml precipitation solution (20% PEG6000, 2.5 M NaCl) and incubation at 37 1C for 1 h followed by centrifugation (5 min, 15,000g). The pellets were washed in 70% EtOH, air dried, and resuspended in TE buffer (10 mM Tris–HCl, 1 mM EDTA, pH 8) at 1 ml TE per gram extracted soil (dry weight equivalent). Extracted DNA was examined by electrophoresis in agarose gels (1% w/v in TBE) and quantified using a fluorescence emission procedure with PicoGreens (Molecular Probes, Eugene, OR, USA). DNA concentration was adjusted to 10 ng ml 1 with TE containing bovine serum albumin (BSA, molecular biology grade, Fluka, Buchs, Switzerland; final concentration 3 mg ul 1) and heated for 2 min at 95 1C to bind PCR inhibiting substances such as humic acids. Bacterial 16S ribosomal RNA genes were PCR amplified according to Hartmann et al. (2005) in a total volume of 50 ml reaction mixture containing 50 ng of total DNA, 0.4 mM dNTPs (Promega), 2 mM MgCl2, 1 PCR-buffer (QiagenGmbH, Hilden, Germany), 0.6 mg ml 1 BSA (Fluka,Buchs, Switzerland), 2 U HotStar Taq-polymerase (Qia- gen) and 0.2 mM of each primer. The bacterial primers used were the forward, fluorescently labelled primer27F (FAM-labelled forward primer, position on 16S rRNA 8-27 (Escherichia coli numbering, corresponding GenBank entry: J01695), 50 -AGAGTTTGATCMTGGCT- CAG-30 ) and 1378R (unlabelled reverse primer, position1378-1401, 50 -CGGTGTGTACAAGGCCCGGGAACG-30 ) (Heuer et al., 1997). PCR amplification was performed with a PTC-100 thermocycler (MJ Research, Waltham,MA, USA) with the following cycling conditions: an initial activating step for HotStar Taq-polymerase (15 min at95 1C), followed by 35 cycles with denaturation at 94 1C for45 s, annealing at 48 1C for 45 s, extension at 72 1C for

2 min. The PCR amplification was then ended by an

1 B. Frey et al. / Soil Biology & Biochemistry 38 (2006) 1745–B. Frey et al. / Soil Biology & Biochemistry 38 (2006) 1745– 1additional final extension step at 72 1C for 5 min. Amplified DNA was verified by electrophoresis of aliquots of PCR mixtures (5 ml) on a 1% agarose gel in 1% TBE buffer.

2.9. T-RFLP analysis

To obtain a maximum number of terminal restriction fragments that were well separated in capillary electrophoresis, we tested the restriction enzymes MspI, HaeIII, HhaI, and combinations of them. MspI and the combination of HaeIII and HhaI gave the best results (see also Sessitsch et al., 2001) and were used throughout our study. Digestions were carried out in a total volume of 45 ml containing 22.5 ml of PCR product (45 ml subdivided into two parts), 2 U of restriction enzyme (Promega) in 1% Y Tango buffer (diluted with HPLC water) and incubation for 3 h at 37 1C. Aliquots (5 ml) of digestion products were verified on a 2% agarose gel in 1% TBE buffer. Finally, the digestions were desalted with Millipore Montage-PCR microspin columns (Millipore, Volketswil, Switzerland), according to the manufacturer’s instructions. T-RFLP analyses were performed according to Hartmann et al. (2005). Briefly, 1 ml restriction digests were mixed with0.4 ml of the internal size standard ROX500 (Applied Biosystems, Inc., Foster City, USA) and 12 ml of forma- mide (Applied Biosystems), denatured at 92 1C for 2 min, chilled on ice for 5 min, and separated on an ABI Prism 310Genetic Analyzer (Applied Biosystems) equipped with a36 cm capillary and POP 4 polymer (Applied Biosystems). The size of the terminal restriction fragments (T-RF) given in relative migration units (rmu) and peak heights were determined with the GeneScan analysis software version3.1 (Applied Biosystems) with peak detection set to 50 fluorescence units. Peak signals were converted into numeric data for fragment size and peak height by using the Genotyper 3.6 NT (Applied Biosystems). If it was not possible to unambiguously determine the height of a specific peak (e.g. if there was a peak shoulder), the peak was omitted from the analysis of all samples. The peak heights were recorded and compiled in a data matrix for statistical analysis. T-RF peak heights were normalized by dividing the peak heights of the single T-RFs by the sum of the total peak heights of all T-RFs according to Blackwood et al., (2003) and Hartmann et al. (2005). In a second step centering corresponding peak values across all samples assigned all peaks the same weight (mean ¼ 0, standard deviation ¼ 1).

2.10. Statistical analysis

Variables were tested for normality. Data that were not normally distributed or showed unequal variance were transformed prior to analysis using square-root or log transformation. Statistical analyses were carried out using ANOVA and MANOVA for repeated measures (time as repeated measures variable) and multiple comparisons of significant differences were made with the Tukey test


ðpp0:05Þ using SYSTAT 10 (Statsoft inc., Tulsa, OK, USA).

Discriminative statistics of the T-RFLP data were performed by one-way ANOVA. Each pairwise combination of soil treatments (acid effect: CO/AR and HM/ HMAR; metal effect: CO/HM and AR/HMAR), was compared with respect to significantly changing peaks. Peaks which were only changing due to HM treatment with no further interaction between AR and HM treatment were

Table 2Results of the statistical analysis for chemical and microbiological variables obtained from MANOVA with repeated measures (**po0:01; * 0 : 0 1 o p o 0:05; n.s. ¼ not significant)

MANOVA

Within

Variable Treatment Between Time Time Treatment

aconsidered to be a HM effect indicator. Similarly, peaks which were only changing due to AR treatment with no

pH value Acid effectMetal effectb

n.s. ** n.s.* ** *

further interaction between AR and HM treatment were considered to be an AR effect indicator.

Both PLFA and T-RFLP profiles of each sample were compared using multivariate statistical methods. Initial detrended correspondence analysis (DCA) indicated that the data exhibited a linear, rather than a unimodal, response to the environmental variables (HM treatment, irrigation acidity and time), justifying the use of linear ordination methods (Leps and Smilauer, 2003). Therefore, we used principal component analyses (PCA) using CANOCO software for Windows 4.5 (Microcomputer Power, Ithaca, NY) with the aim of identifying the samples which generate similar patterns. We then tested the effects of HM treatment, irrigation acidity and time on microbial community composition with redundancy analysis (RDA) using the CANOCO software (Marschner et al., 2003; Kennedy et al., 2004; Hartmann et al., 2005). A Mantel test was performed in order to examine similarities between bacterial PLFA and T-RFLP profiles. The Mantel test evaluates the null hypothesis of no correlation between two distance matrices that contain the same set of sample units (Mantel, 1967).

3. Results

3.1. Soil chemical analysis

MANOVA with repeated measures revealed significant time and time x metal effects for pH, bioavailable Cu and Zn (Table 2). There were no significant ðp40:05Þ pH variations in the soils between the treatments at the end of the experiment, although soil pH was lower ðpo0:05Þ in all treatments compared to the values at the beginning (Table 3). Bioavailable Cu, Pb and Zn were assessed with heavy metal-specific bacterial biosensors (Table 3). There was a very slight but significant ðpo0:05Þ reduction in HM bioavailability with time except for Pb ðp ¼ 0:131Þ. Con- trary to our hypothesis, AR did not significantly increase the bioavailability of the HMs (Cu, Pb and Zn) in the topsoil (Table 2).

3.2. Microbial biomass and community function

At the end of the experiment (2003), the HM treated soils contained on average 194 mg Cmic g

1 dry soil in the HM treatment and 151 mg Cmic g

1 dry soil in the combined

Cu Acid effect n.s. ** n.s.Metal effect ** ** **

Pb Acid effect n.s. n.s. n.s.Metal effect ** n.s. n.s.

Zn Acid effect n.s. * n.s.Metal effect ** ** **

Cmicc Acid effect n.s. n.s. n.s.

Metal effect ** n.s. n.s.

Respiration Acid effect n.s. * n.s.Metal effect ** ** *

aA ¼ AR and HMAR.bB ¼ HM and HMAR.cMicrobial biomass C.

treatment (HMAR), which was 40% less than the untreated control soil (CO), which contained343 mg Cmic g

1 (Table 4). This strong decrease in themicrobial biomass C due to the HM amendment wasobserved at all sampling times (data not shown). The strongest effect ðpo0:05Þ was induced by the combined treatment (HMAR) resulting in the lowest microbial biomass C containing 151 mg Cmic g

1 dry soil, whereas AR had only a minor effect on the microbial biomass C compared to the control containing 301 mg Cmic g

–1 dry soil. In accordance with the Cmic data, total PLFA contents were influenced by the addition with the HMs but not by AR (Table 4). The average total PLFA content in the HM amended soils were 43.3 nmol g 1 dry soil (HM) and43.1 nmol g 1 dry soil (HMAR), which was significantly lower than in the non-HM treated soils at the end of the experimental period (74.4 nmol g 1 dry soil for CO and74.3 nmol g 1 dry soil for AR, respectively). BacterialPLFAs were the predominant fatty acids in all soil samples analysed (Table 4). The most abundant bacterial PLFAs in the control were 18:1o7, 16:1o7 which are typical for Gram-negative bacteria and i15:0, a15:0, which are typical for Gram-positive bacteria (Table 5). Indicator PLFAs for Gram-negative and Gram-positive bacteria were significantly affected by the HM amendment showing an approximately 50–60% reduction in microbial biomass. This effect was not more pronounced in the combined HM and AR treatment (Tables 4 and 5). Interestingly, AR stimulated the Gram-positive bacteria compared to the control. The specific Gram-positive PLFA contents aver- aged 15.9 nmol g-1 dry soil in the AR treatment which was


Table 3Soil pH (0.01 M CaCl2) and bioavailable Cu, Pb and Zn (mg g 1 dry soil) measured by heavy metal-specific bacterial biosensors (BIOMET) in the topsoil during the course of the experiment (mean7SD; n ¼ 4)

pH (0.01 M CaCl2) Cu (mg g 1) Pb (mg g 1) Zn (mg g 1)

2000b 2000 2001 2003 2001 2002 2003 2001 2002 2003 2001 2002 2003

COa 6.570.1 6.570.2 6.070.2 5.670.4 171 171 171 nd nd nd 271 271 271AR 6.670.1 6.570.1 5.870.3 5.870.7 170 272 171 nd nd nd 271 171 271HM 7.270.1 6.870.1 6.370.2 6.170.4 76724 33717 21714 1479 775 574 354780 320761 285754HMAR 7.270.2 6.670.1 6.270.2 5.970.5 63721 40715 35717 1175 774 673 388763 312757 291750

aTreatments: CO ¼ control; AR ¼ acid rain; HM ¼ heavy metal treatment; HMAR ¼ combined treatments (heavy metal and acid rain).bFirst sampling was taken in spring 2000 just before trees were planted. Other sampling times were taken in autumn. nd ¼ not detected.

Table 4Microbial biomass C (Cmic), total PLFAs, total bacterial PLFA markers, indicator PLFAs for Gram- positive and Gram-negative bacteria and total fungal PLFA marker in the four treatments at the last sampling (mean7SD; n ¼ 4)

Treatment Cmic (mg g 1 dry soil)

Total PLFA(nmol g 1 dry soil)

Bacterial PLFA (nmol g 1 dry soil)

Gram-positive(nmol g 1 dry soil)

Gram-negative(nmol g 1 dry soil)

Fungal PLFA (nmol g 1 dry soil)

COa 343736ab 74.477.8a 36.573.1a 13.770.5b 17.672.1a 4.570.8aAR 301733a 74.378.8a 38.773.2a 15.971.1a 18.171.9a 5.671.0aHM 194720b 43.372.7b 19.971.0b 7.770.6c 8.870.6b 2.370.4bHMAR 151717c 43.177.7b 17.372.8b 6.870.7c 7.470.9b 2.170.8b

aTreatments: CO ¼ control; AR ¼ acid rain; HM ¼ heavy metal treatment; HMAR ¼ combined treatments (heavy metal and acid rain).bMean values followed by the same letter are not significantly different according to ANOVA and multiple comparisons with Tukey test ðp 0:05Þ.

Table 5Comparison of mean values for indicator phospholipid fatty acids for Gram- positive and Gram-negative bacteria expressed as nmol g 1 dry soil ðn ¼ 4Þfrom the four treatments on the final sampling occasion

Gram-positive PLFA Gram-negative PLFA FungPLFA ActPLFA

i15:0 a15:0 i16:0 i17:0 a17:0 10Me17:0 16:1o7c 16:1o5 cy17:0 cy19:0 18:1o7 18:2o6,9 10Me18:0

COa 4.470.3bb 4.170.2a 2.070.1a 1.470.0a 1.470.1a 0.570.1a 4.070.5a 2.270.2a 1.870.4a 0.270.0a 9.472.4a 4.570.8a 5.271.2aAR 5.370.5a 4.870.5a 2.270.2a 1.570.1a 1.570.1a 0.670.1a 4.170.6a 2.270.3a 2.370.4a 0.270.0a 9.271.4a 5.671.0a 4.770.7aHM 2.570.1c 2.270.2b 1.170.1b 0.870.1b 0.870.0b 0.370.1b 2.070.2b 1.170.1b 1.170.1b 0.170.0b 4.670.6b 2.370.6b 3.370.6bHMAR 2.170.5c 2.070.5b 1.070.2b 0.770.2b 0.870.2b 0.270.1b 1.870.3b 1.170.2b 0.670.5b 0.170.0b 3.970.9b 2.171.2b 3.170.2b

aTreatments: CO ¼ control; AR ¼ acid rain; HM ¼ heavy metal treatment; HMAR ¼ combined treatments (heavy metal and acid rain).bMean values followed by the same letter are not significantly different according to ANOVA ðp 0:05Þ.

16% more as compared to the control soil (13.7 nmol g-1 dry soil). The fatty acid i15:0 for Gram-positive bacteria was stimulated by AR, whereas all other bacterial PLFA markers were not affected by AR (Tables 4 and 5). All of the soils contained small quantities of the fungal marker PLFA 18:2o6,9 (Tables 4 and 5). The fungal biomass was strongly depressed by the HM in the topsoil showing a40–50% decrease in biomass, whereas AR tended to increase fungal biomass. In addition, there was a small amount of the actinomycete marker 10Me18:0 in all of the soils (Table 5). The contents of the actinomycete marker10Me18:0 were significantly lowered in the HM-treated soils as compared to the non-HM-treated soils (C and AR).

The addition of HM-containing filter dust also strongly affected the community function as measured by basal

respiration (Fig. 1) and soil hydrolase activities (data not shown). Basal respiration rates showed a 50% decrease on average in the HM-treated (HM and HMAR) soils compared to the non-HM-treated soils (CO and AR). This drastic decrease was observed at all sampling times (Fig. 1). Phosphatase, b-glucuronidase and N-acetyl-b-glucosaminidase showed 71%, 88% and 64% inhibition in the metal- treated (HM and HMAR) soils compared to the non-HM treated soils (CO and AR). In contrast, AR exerted only a non-significant tendency to decrease in basal respiration compared to the CO (Fig. 1), whereas all soil enzymatic parameters except for N-acetyl-b-glucosaminidase were significantly ðpo0:05Þ decreased compared to the CO. Interestingly, N-acetyl-b-glucosaminidase activity slightly increased in the AR treatment.

PC

2 (7

µ

g C

O2

h-1

g-1

dry


4

3.5 a a

CO AR HMHMAR

Table 6Results of the Monte Carlo Permutation test in percent of variation explained and significance of environmental factors (**po0:01;

a a *0:01opo0:05; n.s.¼ not significant) for the microbial community

a3

a a

a

2.5c

2 b b b

c b

1.5 c c

composition in the soils determined by PLFA and T-RFLP data

PLFA T-RFLP

% p % p

Acid effecta 2 n.s. 17 ** Metal effectb 67 ** 52 ** Time effectc -d - 7 *

aAcid ¼ AR and HMAR.1 bMetal¼ HM and HMAR.

0.5cTime ¼ yearly sampling times (2001, 2002, 2003).dPLFA data from 2003 only.

02000 2001 2002 2003

Fig. 1. Development of soil basal respiration in the four treatments over the experimental period. Means of four replicate chambers and standard deviations of the means are shown. Letters above the bars indicate significant differences at pp0:05 within each separate yearly analysis.

0.8

i17:0, a17:0, 10Me17:0) and Gram-negative (16:1o7,16:1o5, cy17:0, 18:1o7, cy19:0) bacteria contributed considerably to the variation in each direction along the PC 1-axis. To assess the overall importance of the two measured environmental factors (acid and HM), the mean percent explanation was calculated (Table 6). Only the metal variables used in the redundancy discriminate analysis had a significant effect on the microbial community composition.

3.4. Effect on T-RFLP profiles

-0.6

-1.5 1.5

PC 1 (75 %)

Bacterial community profiles were determined by using genetic fingerprinting with T-RFLP of the 16S rDNA. T-RF lengths ranged from 50 to 500 bp for MspI and from50 to 420 bp for HaeIII /HhaI derived fingerprints. Analysis of the bacterial community structure with T-RFLP identified 43 and 71 operational taxonomic units

Fig. 2. Score plots of the two first components (PC) in a principal component analysis of the log mol% of microbial PLFAs at the last sampling. The four treatments were: filled circles: controls; empty circles: acid rain; filled triangles: heavy metal; empty triangles: heavy metal and acid rain. Quadruplicates samples of each treatment were analysed.

3.3. Effect on PLFA profiles

To elucidate major distributions patterns, a PCA of PLFA data was performed. PLFA profiles (including all 38 detected fatty acids) of samples from the end of the experiment (2003) are presented. In the score plot of the PCA, the HM-treated soils (HM; HMAR) were separated from the non-HM-treated soils (CO; AR) along the first principal component (Fig. 2). PC1 accounted for 75% of the total sample variance. Monte Carlo permutation analysis revealed that separation was highly significant ðpo0:01Þ. Within the non-HM-treated soils, there was no clear separation between controls (CO) and AR treatment. In addition, within the HM-treated soils there was no clustering of treatment replicates (po0:05; HM versus HMAR; Fig. 2). In the loading plot (data not shown) typical PLFAs from Gram-positive (i15:0, a15:0, i16:0,

(OTU) after digestion with MspI and HaeIII /HhaI, respectively. Since in almost all cases T-RFLP analyses of the two different restriction enzymes (MspI versus HaeIII / HhaI) followed the same pattern, only results for HaeIII / HhaI are presented hereafter. T-RFs that significantly discriminated the different treatments were identified using one-way ANOVA (Table 7). Fourty per cent of the peaks differed between the AR treatment (average on the AR and HMAR data), whereas 57% of the peaks differed between the HM treatment (average on the HM and HMAR data). Ten per cent of all peaks changed significantly based on the HM treatment without revealing AR HM treatment interactions, whereas only 7% of the peaks changed significantly based on the AR treatment without revealing AR HM treatment interactions (Table 7). Besides the effects of HMs and AR on the bacterial community structures, T-RFLP analysis also detected time-dependent shifts during the experiment period (Fig. 3). Between 31% and 45% of the peaks differed between the first and last sampling date both in the HM-treated and non-HM- treated soils (Table 7). PCA of T-RFLP data revealed that the non-HM treated soils were separated from the HM treated soils (Fig. 3). These groups were separated

PC

2


Table 7Frequency of significantly differing T-RFs observed in the four treatments as determined with ANOVA at the last sampling. T-RFLP profiles basedon restricti o n of PCR produc t s with restricti o n enzym e s HaeIII/Hh a I

Total number of detected peaks 71 (100)a

Changing with treatmentsAcid effect: Peaks differing between CO and ARb,c 27 (38) Acid effect: Peaks differing between HM and HMAR 29 (41) Metal effect: Peaks differing between CO and HM 39 (55) Metal effect: Peaks differing between AR and HMAR 41 (58)

Changing with timePeaks differing between CO01 and CO03d 25 (35) Peaks differing between AR01 and AR03 26 (37) Peaks differing between HM01 and HM03 22 (31) Peaks differing between HMAR01 and HMAR03 32 (45)

e

time and the AR treatment influenced the bacterial community patterns. Soils from AR 2002 and AR 2003 were significantly ðpo0:05Þ separated from the other soils (CO at all time points and AR 2001) along PC 2, which accounted 23% of variation in the data. Within the group of HM treated soils, the combined treatment HMAR at later sampling times was significantly ðpo0:05Þ located apart from the rest of the treatments (HM at all time points and HMAR 2001). At all sampling dates both variables (acid and HM) used in the redundancy discriminate analysis had a significant effect on the bacterial community composition (Table 6). Testing the individual factors for their contribution to total variance showed that HM contamination had the strongest effect, explaining 52% of the observed variance, while 17% of the variance was

Potential metal indicator 7 (10) explained by acidic irrigation. In addition, time explained aPotential acid rain indicatorf 5 (7) low but significant ðpo0:05Þ effect on the bacterial

aPercentages of total numbers are indicated in parentheses.bPeaks which were significantly ðp 0:05Þ changing in their heights.cTreatments: CO ¼ control; AR ¼ acid rain; HM ¼ heavy metal

treatment; HMAR ¼ combined treatments (heavy metal and acid rain).dThe number behind treatments 01 or 03 refers to the first or last

sampling date.ePeaks which were only changing due to heavy metal treatment and

revealed no interaction between AR and HM treatment.fPeaks which were only changing due to acid rain and revealed no

interaction between AR and HM treatment.

1.5

-1.0

-1.0 1.5

PC 1 (37 %)

Fig. 3. PCA score plot of the bacterial T-RFLP data. All time points were analysed simultaneously. The four treatments were: filled circles: controls; empty circles: acid rain; filled triangles: heavy metal; empty triangles: heavy metal and acid rain. Size of symbol represents the three sampling dates: smallest symbols ¼ year 2001, medium ¼ year 2002, largest symbols ¼ year 2003. Quadruplicates samples of each treatment were analysed.

primarily by PC 1, which accounted for 37% of variation in the data. Monte Carlo permutation analysis revealed that separation was highly significant ðpo0:01Þ. Within the group of soils not treated with HMs, both the sampling

community composition. A Mantel test was used to test the significance of the correlation between PLFA-based community structure and T-RFLP-based community structure. Mantel test analyses revealed only a weak correlation ðr ¼ 0:38; po0:05Þ between the two distance matrices of the T-RFLP and PLFA (only bacterial PLFAs were included) fingerprinting methods.

4. Discussion

In this study we used a polyphasic approach combining community function analyses and community profiling techniques to evaluate the toxicity of a HM containing filter dust to the indigenous soil microbial communities during reforestation. Our study clearly showed that exposure to HMs for the 4-year experimental period negatively affected soil microbial activities and changed microbial community structures. To the best of our knowledge, this is the first study in which the combined effects of HM contamination and AR with subsequent reforestation on soil microbial community function and structure have been examined. Bioavailable HM concentrations (Cu, Pb and Zn) remained high in the HM- contaminated soils and only slightly decreased as measured with the HM-specific bacterial biosensors. Measurements with HM-specific bacterial biosensors are known to give detailed information of the risk and toxicity to soil microorganisms and represent bioavailable HM contents in the soils (Perkiomaki et al., 2003; Turpeinen et al., 2004; Renella et al., 2004). A reduction in bioavailable HM contents in soil does not necessarily mean loss, it may also indicate transformation to less soluble forms. In fact, Voegelin et al. (2005) have shown that transformation of HM forms took place in the HM-contaminated forest model ecosystem, which might have influenced the bioavailability of the HM in the soils and the toxicity to soil microorganisms. Several authors have studied the soil microbial communities in HM-polluted soils treated with fly ash, liming or a mixture of compost and wood chips (Kiikkila et al., 2001; Kelly et al., 2003; Perkiomaki et al.,


2003). These treatments increased the soil pH and decreased the solubility of HM in the soils, resulting in positive effects on the soil microbial communities. As long as the HMs are not removed or immobilized they may be potentially toxic to the indigenous soil microorganisms. In fact, the HM contents remained high during the experimental period and were above the guide level of the Swiss ordinance relating to impacts on soil (OIS, 1998). Inter- estingly, the high HM contents in the topsoil did not exert negative effects on tree growth, since aboveground biomass production of the tree species was not found to be reduced (Hermle, 2004). In contrast, the presence of HMs affected the fine root growth by reducing the root density in the topsoil (Menon et al., 2005). Whether this reduced fine root growth in the topsoil have influenced the activity of the soil microbial community cannot not be ruled out.

The measurement of PLFAs, together with nucleic acid- based molecular techniques for fingerprinting the 16S ribosomal DNA (rDNA) component of bacterial cells, provided complementary information on the soil microbial communities. Both community-level profiling techniques were able to discriminate HM effects. The ordination plots of the microbial profile revealed the extent to which HM contamination had shifted the microbial communities (Figs. 2 and 3). Turpeinen et al. (2004) also successfully used PLFA profiling as well as 16S rDNA community profiling with T-RFLP in a microcosm experiment, but without plant growth, to follow the effects of HM treatment of soils on the soil microbial communities. The HM-induced community differences clearly persisted for the duration of the experiment as shown by the T-RFLP profiles, which separated HM treated from non-HM treated soils at all sampling times (Fig. 3) indicating that the microbial communities did not show evidence of convergence in community structure between treatments. Our study is in accordance with others that have reported harmful residual effects of HMs on the soil microbial communities persisting over several years under field conditions (Sandaa et al., 1999; Kandeler et al., 2000; Moffett et al., 2003; Abaye et al., 2005). By cloning and sequencing analysis Sandaa et al. (1999) and Moffett et al. (2003) found a lower microbial diversity in the soil after long-term applications of HMs. Whether decreased bacterial diversity in the HM-treated soils occurred in our study cannot be determined with our genetic fingerprint analyses. According to the T-RFLP analyses, the total number of fragments (taxonomic units) was not reduced in the HM- treated soils as compared to control soils. If T-RFLP profiles differ (HM versus control), then bacterial communities differ in species composition, or bacterial communities have the same species present, but in different proportions. Our study shows that both cases occurred in the T-RFLP profiles. Diaz-Ravina and Baath (1996) suggested that at high bioavailable HM levels, the disappearance of HM-sensitive bacteria is probably re- sponsible for the decrease in microbial activity and the competitive advantage of more HM tolerant ones resulted

in a change in community composition. However, the changed bacterial community in the HM-treated soils may not result in any net effect on broad microbial indices such as basal respiration or total biomass compared to control soils even after 4 years of reforestation. With our T-RFLP analysis we were able to identify TRFs which were only changing due to HM treatment (potential metal indicators). However, one of the main limits of the T-RFLP technique is the difficulty of obtaining taxonomic information of the organisms for a particular TRF. Database matching of TRF sizes is imprecise and may not produce species- or even genus-specific assignment, and results can be experimentally verified indirectly only after a long screening of a 16S rDNA library. Recently, a cloning method for taxonomic interpretation of T-RFLP patterns was introduced (Mengoni et al., 2002). This method is particularly useful when a detailed taxonomic description of a bacterial community such as potential HM indicators derived from a TRF pattern is needed.

Shifts in the microbial community structures following the HM treatment were also demonstrated by the PLFA analyses. The HM-treated soils had lower levels of the specific PLFA markers for both Gram-positive and Gram- negative bacteria as compared to soils from the non-HM- treated soils. Interestingly, the former ones were not found to be more HM tolerant as reported by other workers (Wenderoth and Reber 1999; Sandaa et al., 1999; Abaye et al., 2005). In addition, no specific bacterial PLFA was detected, which increased due to HM amendment as shown by others (Frostegard et al., 1993b; Pennanen et al., 1996; Baath et al., 1998). Similarly, we also found that the PLFA specific for fungi were decreased to the same extent as the specific bacterial PLFA markers by the HM treatment. However, we cannot rule out whether this was due to direct effects of HMs on fungi or indirect due to less root growth. Other results in the literature indicate that fungi can respond differently to elevated HM contents with a decrease (Pennanen et al., 1996) or increase in the fungal- specific PLFAs (Frostegard et al., 1996; Kandeler et al.,2000; Rajapaksha et al., 2004; Turpeinen et al., 2004). Ectomycorrhizal fungi may have a protective effect for trees in metal polluted soils (Frey et al., 2000). However, in the present study a significant benefit of ectomycorrhizal fungi to trees exposed to HMs was not expected since ectomycorrhizal biomass was assumed to be low in our arable soil (pH 6.5) planted with young trees.

Contrary to our expectation that AR would increase the bioavailability of the HM in the contaminated topsoils, and thus amplify the HM-induced stress effects, the combined treatment (HMAR) resulted only in marginal effects on total microbial biomass and basal respiration rate compared to the HM-treated soils without AR. This is very likely due to a good buffering of the acid input in the topsoil as indicated by the similar soil pH in all treatments at the end of the experiment (Table 3). This may explain why there was no increase in bioavailable HM contents in the topsoil. Therefore, in all the microbial activities


measured in the HM-treated soil, a significant proportion of the inhibition can be attributed to HM and not to AR.

Not only were significant changes in the microbial community structures found in the HM-treated soils, but T-RFLP analysis was also able to differentiate the bacterial community of the control soils from that of the AR treated ones (Fig. 3), in which no effect on soil pH or on microbial biomass or activities could be detected. In contrast, PLFA analyses showed a relatively close similarity between the control and AR treatment. Mantel test analyses also revealed a weak correlation ðr ¼ 0:38; po0:05Þ between the two distance matrices of the T-RFLP and bacterial PLFA fingerprinting methods, although both community profiling methods distinguished between the soil microbial communities of the HM-treated soils and those of the controls. The PLFA analysis was shown earlier to be efficient in detecting the effects of decreasing soil pH on the soil microbial community (Pennanen et al., 1998). The abundance of PLFA common to Gram-positive bacteria was increased in the AR treatment as compared to the control (ambient irrigation). Most of these Gram-positive specific PLFAs were not significantly affected except for the i15:0. This is in accordance with Pennanen et al. (1998), who found branched fatty acids typical of Gram-positive bacteria to be increased due to acidification. However, whether this situation represents a direct pH effect or an indirect effect by modified root exudation due to the acids and thus altering carbon availability for microbes, cannot be elucidated from the present results. Fungal biomass appeared to respond to the AR treatment since quantities of the fungal PLFA 18:2o6,9 tended to increase in AR. However, previous studies have reported an unchanged fungal biomass due to simulated AR in forest soils with low soil pH (Pennanen et al., 1998; Perkiomaki et al., 2003). Similarly, AR treatment stimulated the fungal activity as shown by the increase in N-acetyl-b-glucosaminidase activity which is suggested to be correlated with the fungalactivity.

Time-dependent shifts of the soil microbial communities appear to be represented in the T-RFLP-based PCA of the different sampling times (Fig. 3). Patterns at earlier times (2001) were separated from those at later times (2003), which may be attributed mainly to plant growth. The establishment of tree seedlings led to a slightly decreased pH (of 1 unit) in the topsoil with time independently of the treatments. This decrease in soil pH over time may have resulted from nutrient uptake of the growing trees and changes in both the amount and composition of root exudates which change with plant age and/or plant developmental stage (Kozdroj and van Elsas, 2000; Baudoin et al., 2003). Our results are therefore in accordance with other studies in which temporal changes in rhizosphere microbial community composition occurred in annual plants (Lukow et al., 2000; Smalla et al. 2001; Marschner et al., 2002). Plants may also modify their rhizospheres in response to certain environmental signals and stresses (Ryan et al., 2001). It might be expected that

roots grown in a HM-contaminated soil will change their root exudates and thus establish different communities in the root zone. Roots are also known to avoid metal- contaminated areas in the soil (McGrath et al., 2001). In fact, Menon et al. (2005) found in the presence of HM a reduced root growth in the topsoil and a weak, but nonetheless significant, effect on the soil water relations of the investigated juvenile forest ecosystem. Therefore, besides of a direct toxic effect of the HM on the soil microbial communities, a smaller carbon release from the roots (Jones, 1998) could be one of the reasons why we have found less microbial activities in the HM-treated soils.

5. Conclusions

Microbial community analysis combined with community function assays were useful in assessing the effects of chronic heavy metal (HM) contamination on soil microbial communities in a newly established forest ecosystem. Both community-level profiling techniques were very powerful in discriminating HM effects. Microbial communities present in the HM-contaminated soil may have been shifted to a more HM tolerant but probably ineffective microbial community. The changed microbial community did not fulfil the community function of the microbial community in the uncontaminated soils even after 4 years of reforestation. Such data may help to improve the accuracy of the estimation of the benefit and risk when HM-polluted soils are regreened by woody plants.

Acknowledgements

The authors would like to thank Andreas Rudt for his technical assistance in the laboratory; Madeleine Goerg, Peter Bleuler and Michael Lautenschlager for their help in carrying out the experiment, Martin Hartmann for his support in the statistics and Peter Christie (Queen’s University Belfast) for reviewing earlier versions of this manuscript. The central laboratory of WSL (accreditation number ISO 17025) is acknowledged for performing ICP- AES analyses. This research was supported by the Swiss Secretariat for Education and Research, COST Action 631 (UMPIRE).

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