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Soil Biology & Biochemistry 38 (2006) 1745–1756
www.elsevier.com/locate/soilbio
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
a
Soil Ecology, Swiss Federal Research Institute WSL, CH-8903 Birmensdorf, Switzerland
b
University of Agricultural Sciences Vienna (BOKU), A-1180 Vienna, Austria c
Molecular
Ecology, Agroscope FAL Reckenholz, CH-8046 Zu¨ rich, Switzerland
Received 23 June 2005; received in revised form 14 October 2005; accepted 14 November 2005
Available 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 rRNA
gene
1. Introduction
Microbial communities play important roles in soil
because of the many functions they perform in nutrient
cycling, plant symbioses, decomposition, and other ecosys-
tem 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 ecosys-
tem 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 73925 41; fax: +411 739 2215.
E-mail address: beat.frey@wsl.ch (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 follow-
ing 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

1746 B. Frey et al. / Soil Biology & Biochemistry 38 (2006) 1745–1756B. Frey et al. / Soil Biology & Biochemistry 38 (2006) 1745–1756 1746
Sand (%) Silt (%) Clay (%) pH CaCl2 Þð
a
CEC (mmolc 1 b 1 c
Topsoil 36 49 15 6.6 102 99.9 15.1 1.5
Subsoil 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 experi-
mental 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 microorgan-
isms. 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 respira-
tion and soil enzymatic activities (Zimmermann and Frey,
2002). The changes in the microbial community structures
were determined by two fingerprinting techniques: poly-
merase 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 contents
averaged 740 mg kg 1
Cu, 3000 mg kg 1
Zn, 22 mg kg 1
Cd
and 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 1
Selected physical and chemical properties of the soil materials used at the beginning of the experiment (2000)
1
kg ) Base saturation (%) Corg (g kg ) Ntot (g kg )
a
CEC ¼ cation exchange capacity.
b
org ¼ organic.
c
tot 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-glucosaminide
500 mM, MU-b-glucuronide 250 mM and MCA-leucine
1500 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 quantifi-
cation 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 metha-
nolysis 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 chloro-
form/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 detected
38 PLFAs and was used to indicate the total microbial
biomass. 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 and
10Me18:0 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 and
10Me17:0 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% PEG
6000, 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 concentra-
tion 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 (Qiagen
GmbH, 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 primer
27F (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, position
1378-1401, 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 at
95 1C), followed by 35 cycles with denaturation at 94 1C for
45 s, annealing at 48 1C for 45 s, extension at 72 1C for
2 min. The PCR amplification was then ended by an
additional 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 electro-
phoresis, we tested the restriction enzymes MspI, HaeIII,
HhaI, and combinations of them. MspI and the combina-
tion 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 with
0.4 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 310
Genetic Analyzer (Applied Biosystems) equipped with a
36 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 version
3.1 (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 combina-
tion 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 2
Results of the statistical analysis for chemical and microbiological
variables obtained from MANOVA with repeated measures (**po0:01;
*0:01opo 0:05; n.s. ¼ not significant)
MANOVA
Within
Variable Treatment Between Time Time Treatment
a
considered to be a HM effect indicator. Similarly, peaks
which were only changing due to AR treatment with no
pH value Acid effect
Metal 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 ** ** **
Cmic
c
Acid effect n.s. n.s. n.s.
Metal effect ** n.s. n.s.
Respiration Acid effect n.s. * n.s.
Metal effect ** ** *
a
A ¼ AR and HMAR.
b
B ¼ HM and HMAR.
c
Microbial biomass C.
treatment (HMAR), which was 40% less than the
untreated control soil (CO), which contained
343 mg Cmic g 1
(Table 4). This strong decrease in the
microbial biomass C due to the HM amendment was
observed 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) and
43.1 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 and
74.3 nmol g 1
dry soil for AR, respectively). Bacterial
PLFAs 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 signifi-
cantly 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 3
Soil 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 271 271 271
AR 6.670.1 6.570.1 5.870.3 5.870.7 170 272 171 nd nd nd 271 171 271
HM 7.270.1 6.870.1 6.370.2 6.170.4 76724 33717 21714 1479 775 574 354780 320761 285754
HMAR 7.270.2 6.670.1 6.270.2 5.970.5 63721 40715 35717 1175 774 673 388763 312757 291750
a
Treatments: CO ¼ control; AR ¼ acid rain; HM ¼ heavy metal treatment; HMAR ¼ combined treatments (heavy metal and acid rain).
b
First sampling was taken in spring 2000 just before trees were planted. Other sampling times were taken in autumn. nd ¼ not detected.
Table 4
Microbial 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 4.570.8a
AR 301733a 74.378.8a 38.773.2a 15.971.1a 18.171.9a 5.671.0a
HM 194720b 43.372.7b 19.971.0b 7.770.6c 8.870.6b 2.370.4b
HMAR 151717c 43.177.7b 17.372.8b 6.870.7c 7.470.9b 2.170.8b
a
b
Mean values followed by the same letter are not significantly different according to ANOVA and multiple comparisons with Tukey test ðp 0:05Þ.
Table 5
Comparison of mean values for indicator phospholipid fatty acids for Gram- positive and Gram-negative bacteria expressed as nmol g 1
dry soil ðn ¼ 4Þ
from 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 5.271.2a
AR 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 4.770.7a
HM 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 3.370.6b
HMAR 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
a
b
Mean 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 a
40–50% 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 marker
10Me18:0 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-glucosamini-
dase 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.

1751 B. Frey et al. / Soil Biology & Biochemistry 38 (2006) 1745–1756B. Frey et al. / Soil Biology & Biochemistry 38 (2006) 1745–1756 1751PC2(7%)
µgCO2h-1
g-1
drysoil
4
3.5 a a
CO
AR
HM
HMAR
Table 6
Results 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
a
3
a
a
a
2.5
c
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
*
a
Acid ¼ AR and HMAR.
1 b
Metal ¼ HM and HMAR.
0.5
c
Time ¼ yearly sampling times (2001, 2002, 2003).
d
PLFA data from 2003 only.
0
2000 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 commu-
nity 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 from
50 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

PC2(23%)
Table 7
Frequency of significantly differing T-RFs observed in the four treatments
as determined with ANOVA at the last sampling. T-RFLP profiles based
on restriction of PCR products with restriction enzymes
HaeIII/HhaI
Total number of detected peaks 71 (100)a
Changing with treatments
Acid 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 time
Peaks 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 a
Potential acid rain indicatorf
5 (7)
low but significant ðpo0:05Þ effect on the bacterial
a
Percentages of total numbers are indicated in parentheses.
b
Peaks which were significantly ðp 0:05Þ changing in their heights.
c
Treatments: CO ¼ control; AR ¼ acid rain; HM ¼ heavy metal
treatment; HMAR ¼ combined treatments (heavy metal and acid rain).
d
The number behind treatments 01 or 03 refers to the first or last
sampling date.
e
Peaks which were only changing due to heavy metal treatment and
revealed no interaction between AR and HM treatment.
f
Peaks 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 concen-
trations (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 experi-
mental 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 bacter-
ial 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 commu-
nities differ in species composition, or bacterial commu-
nities 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 indica-
tors). However, one of the main limits of the T-RFLP
technique is the difficulty of obtaining taxonomic informa-
tion 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 fungal
activity.
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 commu-
nity 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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