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Field and microcosm experiments to evaluate the effects of agricultural Cu treatment on the density and genetic structure of microbial communities in two different soils

Lionel Ranjard , Abdelwahad Echairi , Virginie Nowak , David P. H. Lejon , Rachida Nouaïm , Rémi Chaussod
DOI: http://dx.doi.org/10.1111/j.1574-6941.2006.00157.x 303-315 First published online: 1 November 2006


The effects of Cu amendment on indigenous soil microorganisms were investigated in two soils, a calcareous silty clay (Ep) and a sandy soil (Au), by means of a 1-year field experiment and a two-month microcosm incubation. Cu was added as ‘Bordeaux mixture’ [CuSO4, Ca(OH)2] at the standard rate used in viticulture (B1 = 16 kg Cu kg−1 soil) and at a higher level of contamination (B3=48 kg Cu ha−1 soil). More extractable Cu was observed in sandy soil (Au) than in silty soil (Ep). Furthermore, total Cu and Cu-EDTA declined with time in Au soil, whereas they remained stable in Ep soil. Quantitative modifications of the microflora were assessed by C-biomass measurements and qualitative modifications were assessed by the characterization of the genetic structure of bacterial and fungal communities from DNA directly extracted from the soil, using B- and F-ARISA (bacterial and fungal automated ribosomal intergenic spacer analysis). In the field study, no significant modifications were observed in C-biomass whereas microcosm incubation showed a decrease in B3 contamination only. ARISA fingerprinting showed slight but significant modifications of bacterial and fungal communities in field and microcosm incubation. These modifications were transient in all cases, suggesting a short-term effect of Cu stress. Microcosm experiments detected the microbial community modifications with greater precision in the short-term, while field experiments showed that the biological effects of Cu contamination may be overcome or hidden by pedo-climatic variations.

  • field experiment
  • microcosm experiment
  • microbial community
  • Cu impact


In the environment, Cu is widely used as a pesticide against fungal and bacterial diseases in crops or as a contaminant in organic amendments such as pig manure or sewage sludge (Tabatabai, 1977; Christie & Beattie, 1989). Today, in French agriculture, more than 6000 tons of Cu are applied each year by the use of metal-based pesticides and metal-containing organic amendments (Bourrelier & Berthelin, 1998). The combination of recurrent Cu contamination and its strong fixation by certain soil components (organic matter, clay minerals, Fe, Al and Mn oxides) has led to a significant accumulation of Cu in surface soils, sometimes above the EC upper limit (100 mg Cu kg−1 soil) (Stevenson, 1986; Alloway, 1995; Flores-Vélez, 1996). As Cu is known as one of the trace elements with the most deleterious effects on living organisms in soil (Renella et al., 2002), such an accumulation could lead to harmful and irreversible effects on the biological functioning and quality of the soil.

Microorganisms are generally described as more sensitive to Cu and other heavy metal stresses than other organisms in soil biocenosis (Giller et al., 1998). Both the size of the microbial biomass and the soil processes are negatively affected by Cu-based contamination (Berg et al., 1991; Chander & Brookes, 1993; Dahlin et al., 1997; Knight et al., 1997; Giller et al., 1998; Kunito et al., 2001). In contaminated soils, a reduced metabolic potential of indigenous microbial communities is generally observed (Knight et al., 1997), illustrated, for example, by a low respiration rate that can lead to the accumulation of deep layers of organic matter (Rajapaksha et al., 2004), a lower symbiotic N2-fixation with a loss of fertility for legumes (McGrath, 1994), and an incomplete biodegradation of synthetic organic molecules such as pesticides and polycyclic aromatic hydrocarbons (Wall & Stratton, 1994; Sokhn et al., 2001). Cu contamination can also greatly modify soil microbial community structure, as assessed from total phospholipid fatty acid (PLFA) profile analysis (Pennanen et al., 1996; Bååth et al., 1998; Turpeinen et al., 2004). The recent development of DNA fingerprinting methods has provided new insights in the analysis of microbial community structure and diversity modifications due to environmental perturbations (for review, see Ranjard et al., 2000a, b). Using the amplified ribosomal DNA restriction analysis (ARDRA) fingerprint, Smit et al. (1997) observed major modifications of the whole community composition in soil exposed to 750 kg Cu ha−1. Turpeinen et al. (2004) reported that changes in bacterial community t-RFLP (terminal restriction fragment length polymorphism) profiles consecutive to soil contamination with Cu, arsenic and chrome was due to the appearance of new dominating species, probably selected for their resistance. Targeting bacterial domains by using the same approach, Tom-Petersen et al. (2003) revealed that Cu contamination close to the EC safety limit (50–100 mg Cu kg−1 soil) induced stronger shifts in the Rhizobium-Agrobacterium group than in the Cytophaga group.

Most of the studies cited above concerned field experiments which were designed to measure the long-term impact of metal-contaminated waste water, sewage sludge or pig manure. Such contaminations are characterized by the presence of several types of metals (Zn, Cd, Ni, Pb, Hg, Cr, …) associated with inorganic nutrients and organic matter, making it difficult to distinguish the effect of Cu from that of the others, whatever the microbial biomass, microbial processes or community structure considered. Only the experiments conducted by Tom-Petersen et al. (2003) and Smit et al. (1997) provided more comprehensive studies, since they considered field experiments specifically designed to characterize the bacterial community modifications consecutive to Cu spiking. However, these studies were limited to one soil type and did not take into account the fungal community, despite its key role in the biological functioning of soil. Rajapaksha et al. (2004) have recently demonstrated that Cu can differentially affect bacterial and fungal communities, and concluded that the fungal community was more resistant to Cu contamination than the bacterial community.

The aim of our study was to evaluate the impact of agricultural Cu treatment on soil microbial community (bacteria and fungi) in two different soil types, by comparing field and microcosm experiments. Soils were chosen for their contrasting physico-chemical properties, and two levels of Cu contamination were used, B1 (16 kg Cu ha−1) and B3 (threefold rates of B1=48 kg Cu ha−1). B1 contamination corresponded to the upper limit of Cu treatment in French viticulture and allowed the evaluation of the impact of a realistic agricultural practice, whereas B3 allowed the evaluation of the dose–response trend by considering a higher Cu treatment. Quantitative modifications were assessed by means of biomass measurement, whereas qualitative shifts in bacterial and fungal community structure were determined using automated ribosomal intergenic spacer analysis (ARISA) DNA fingerprints from DNA extracted directly from the soil. This technique was used because it allows the rapid examination of the genetic structure of complex bacterial and fungal communities and has been demonstrated to be sensitive and relevant for evaluating modifications in microbial community composition (Ranjard et al., 1997; Ranjard et al., 2000a, b, 2003). The trends of microbial community modifications between microcosm and field experiments were compared in order to (i) confirm if stable laboratory incubations could be a relevant way to detect the impact of metal on soil microbes with more sensitivity and (ii) evaluate the possibility to extrapolate the data from stable laboratory incubations to field applications.

Materials and methods

Site description and soil sampling

Two field trials were set up on two different soil types in Burgundy (France). The first one (Ep) was a calcareous silty clay soil, whereas the second (Au) was a sandy soil. Before the experimentation, Ep soil was under maize crop and Au soil under pasture. The main analytical characteristics of these soils are given in Table 1. These two field experiments consisted of a series of 2 × 2 m plots, maintained without vegetation cover by weekly manual weeding during spring and summer. Cu was added as ‘Bordeaux mixture’ (a mixture of CuSO4 and Ca(OH)2, containing 20% Cu) on eight weekly successive occasions in order to simulate vineyard practices. Four plots received ‘Bordeaux mixture’ at a total rate of 16 kg Cu ha−1 (B1), while four other plots received a total of 48 kg Cu ha−1 (B3). Each plot was divided into 36 (30 cm × 30 cm) subplots and B1 contamination was added by spreading 5 mL of ‘Bordeaux mixture’ (18 g L−1, KB, Ecully, France) in each subplot. For B3 contamination, 15 mL of the same solution was spread. Four additional plots without any Cu addition were used as a control. In each plot, all the soil from the upper layer (0–5 cm) of a subplot was collected at each incubation date. The different incubation dates corresponded to the beginning of the contamination (T0 m), and two (T2 m), four (T4 m) and 12 (T12 m) months after contamination. After sampling, soils were sieved and stored at 4°C for no longer than 2 weeks before use for physico-chemical and biological analyses. We verified that Cu contamination did not affect the soil pH in each plot at each incubation time (data not shown).

View this table:

Soil physico-chemical characteristics of studied soils

(g kg−1)(g kg−1)(%)(%)(%)meq(g kg−1)
  • 1 Corg, soil organic carbon; Norg, soil organic nitrogen; CEC, cation exchange capacity.

Microcosm set up

For microcosm incubations, portions of 50 g of soil were placed in hermetically sealed 2 L glass jars. ‘Bordeaux mixture’ was added in a single pulse to obtain a final concentration of 20 and 60 mg Cu kg−1 soil of Cu for B1 and B3 treatments, respectively, corresponding to the total Cu content measured in the B1 and B3 plots of the experimental fields. This addition of ‘Bordeaux mixture’ did not affect the soil pH (data not shown). For ‘control’ treatments, the soil samples were supplemented with sterile water. The volume of water and ‘Bordeaux mixture’ solutions spread on soils was calculated to reach 80% of the maximum water holding capacity. Soil microcosms were incubated at 20°C for 52 days, with weekly aeration and verification of soil water content to ensure optimal biological conditions. Physico-chemical and biological analyses were performed just after the application of the treatments (t0d) and after 14 (t14d) and 52 (t52d) days of incubation, on triplicate soil microcosms.

Cu measurements

Total and EDTA-extractable Cu contents in each experimental plot of the field experiment were measured at each sampling time by the Laboratory of Soil Analysis (INRA-Arras, France), using normalized methods (NFX 31-120 and NFX 31-147) (Norvell & Lindsay, 1969; Ciesielski et al., 1997, respectively).

Microbial Biomass measurements

The soil microbial biomass was measured by means of the fumigation-extraction method as described elsewhere (Chaussod et al., 1988). Briefly, 40 g of soil from each of the four plots of each field treatment were fumigated with chloroform for 16 h at 20°C. For microcosm samples, fumigation was performed on 20 g of soil. The fumigated and unfumigated soil samples were extracted in 200 mL K2SO4 0.025 M for a 30 min shaking time, and then centrifuged at 5000 g for 10 min. The measurement of soluble organic C in the supernatant was performed with the persulphate-UV oxidation procedure, using Dorhman equipment (Dorhman Co., Santa Clara, CA). A Kec factor of 0.38 was used to convert the amount of extracted DOC to microbial biomass (Chaussod et al., 1988).

Automated RISA fingerprinting

DNA was extracted, purified and quantified from 1 g of soil sampled from each of the four replicates of the field plots (control, B1 and B3) and from triplicate microcosms per treatment using the procedure described by Ranjard et al. (2003). Briefly, 1 g of each soil sample was mixed with 4 mL of a solution containing 100 mM Tris (pH 8.0), 100 mM EDTA (pH 8.0) 100 mM NaCl and 2% (w/v) sodium dodecyl sulfate. Two grams of 106 μm diameter glass beads and eight glass beads of 2 mm diameter were added in a bead-beater tube. The samples were then homogenized for 30 s at 1600 r.p.m. in a mini bead-beater cell disruptor (Mikro-dismembrator S. B. Braun Biotech International) and centrifuged at 7000 g for 5 min at 4°C after a 20-min incubation at 70°C. The collected supernatants were incubated for 10 min on ice with 1/10 volume of 3 M potassium acetate (pH 5.5) and centrifuged at 14 000 g for 5 min. After precipitation with one volume of ice-cold isopropanol, the nucleic acids were washed with 70% ethanol. For purification, aliquots (100 μL) of crude DNA extracts were loaded onto PVPP (polyvinyl polypyrrolidone) minicolumns (BIORAD, Marne la Coquette, France) and centrifuged at 1000 g for 2 min at 10°C. The eluate was collected and purified from residual impurities using Geneclean Turbo kit as recommended by the manufacturer (Q Biogene®, France).

The bacterial and fungal ribosomal IGS (intergenic spacers) were amplified with the primers: S-D-Bact-1522-b-S-20/L-D-Bact-132-a-A-18 and ITS1F/3126 T, respectively, and PCR conditions were as described by Ranjard et al. (2003). Fifty nanograms of DNA were used as template in PCR. B-ARISA and F-ARISA (bacterial and fungal-ARISA) involve the use of a fluorescent-labelled primer for PCR which is the IRD 800 dye fluorochrome (MWG SA Biotech, Ebersberg, Deutschland) for the LiCor® DNA sequencer (Science Tec, Les Ulis, France). PCRs were performed using the S-D-Bact-1522-b-S-20 and 3126 T primers labelled at their 5′ end with the IRD800 fluorochrome. The relative concentration of labelled PCR products was estimated, and between 0.5 and 1 μL of the product was added to deionized formamide and denatured at 90°C for 2 min. ARISA fragments were resolved on 3.7% polyacrylamide gels and run under denaturing conditions for 15 h at 3000 V/60 W on a LiCor® DNA sequencer (Science Tec). The data were analyzed using the 1D-Scan software (Science Tec). The software converted fluorescence data into electrophoregrams, where peaks represented PCR fragments. The height of the peaks was calculated in conjunction with the median filter option and the Gaussian integration in 1D-Scan, and represented the relative proportion of the fragments in the total products. Lengths (in base pairs) were calculated by using a size standard with bands ranging from 200 to 1206 bp.

Statistical analysis

Significant differences (p<0.05) in Cu content and in biomass measurements between treatments and incubation times were determined using Statview-SE software with the Student's t-test.

Data obtained from the 1D-Scan software were converted into a table summarizing the band presence (peak) and intensity (Gaussian area of peak) using the PrepRISA program (Ranjard et al., 2003). To ensure a robust analysis, 100 bands were integrated for B- and F-ARISA profiles with a 2 bp resolution (Ranjard et al., 2003). Principal component analysis (PCA) on a B-ARISA and F-ARISA covariance matrix was performed on the data matrix (bacterial communities as rows and bands as columns). This method provided an ordination of bacterial or fungal communities or of the encoded bands, which were plotted in two dimensions based on the scores in the first two principal components. PCA was performed using the ADE-4 software (Thioulouse et al., 1997). Statistical areas representing 90% confidence were designed over replicates of plots or microcosm samples in a factorial map using the ADE-4 software.


Total and extractable Cu content in experimental field assays

The average total and extractable Cu contents in the surface soil from field plots are presented in Fig. 1. Before Cu contamination (T0), total Cu content in Ep and Au soils was similar (23 mg kg−1 soil) and remained stable in control plots throughout the experiment. For Ep soil at T2 m, the total Cu concentrations reached 47.2 and 79.2 mg kg−1 soil in B1 and B3 plots, respectively, and remained stable until T12 m. For Au soil, concentrations reached 52.3 mg kg−1 in B1 and 95.1 mg kg−1 in B3 at T2 m and decreased to 36.1 and 69.8 mg kg−1 at T12 m, respectively. In terms of concentration in the 0–5 cm layer, the increase in Cu content in the two soils due to B1 (16 kg ha−1) and B3 (48 kg ha−1) levels of contamination were about 20–30 mg kg−1 soil and 50–70 mg kg−1 soil, respectively.


Total and EDTA-extractable copper contents in the surface layer of Ep and Au field plots. Data are means of four independent repetitions of plots±standard deviation. Percentages were calculated by the ratio (Cu EDTA/Cu total) × 100. n.d., not determined.

The average extractable Cu (Cu-EDTA) contents in Ep and Au soils were 4.9 and 11.9 mg kg−1 soil at T0 m, respectively, and remained constant in control plots throughout the experiment (Fig. 1). Cu-EDTA, expressed as a fraction of total-Cu, represented about 20% in Ep soil and 50% in Au soil during the incubation period in control plots (Fig. 1). In B1 plots, Cu-EDTA increased during the first two months up to 15.2 and 35.3 mg kg−1 soil, in Ep and Au, corresponding to about 32.0% and 67.5% of total Cu content, respectively. In Au soil, Cu-EDTA declined during the experimental period to reach 23.3 mg kg−1 soil at T12 m, which correspond to 64.5% of the total Cu (Fig. 1). In B3 plots, the maximum amount of Cu-EDTA is reached at T4 m in Ep soil with 45.5 mg kg−1 soil and at T2 m in Au soil with 77.7 mg kg−1. In both soils, this was followed by a decrease, because at T12 m Cu-EDTA was 32.4 mg kg−1 in Ep soil and 49.3 mg kg−1 in Au soil. Expressed as a percentage, Cu-EDTA represented about 40% of total Cu at each incubation date. In Au soil, it represented 81.7% of total Cu at T2 m and 70.6% at T12 m (Fig. 1).

Biomass content

Regarding the field experiment, the amount of microbial biomass varied according to soil type before Cu contamination (Table 2). Over the year of experimentation, C-biomass in control plots ranged from 322 to 416 mg kg−1 in Ep soil and from 190 to 230 mg kg−1 in Au soil. Cu contamination induced no significant difference in microbial biomass when comparing control, B1 and B3 plots of Ep and Au sites at each sampling time. The only significant variations were observed between sampling dates for a given treatment.

View this table:

C-Biomass measurement for each plot (control, B1 and B3) in each soil and for each sampling date

SoilsBiomass content (mg C kg−1 soil dw)
T0 mT2 mT4 mT12 m
All plotsControlB1B3ControlB1B3ControlB1B3
(± 60)(± 34)(± 46)(± 60)(± 31)(± 46)(± 70)(± 93)(± 63)(± 134)
(± 30)(± 14)(± 39)(± 23)(± 26)(± 25)(± 45)(± 18)(± 27)(± 41)
  • Data are means of four independent repetitions of plots±standard deviation.

  • Superscript letters indicate significant differences at p<0.05 between treatments over the year of experimentation for a given soil.

For soil microcosms, the amount of C-biomass measured before Cu contamination was of a similar order of magnitude as those observed in field samples (Table 3). C-biomass was about 414 mg kg−1 in Ep soil and 175 mg kg−1 in Au soil. No significant difference in C-biomass was observed in control microcosms during the incubation period (Table 3). In contaminated microcosms of Ep soil, a slight but significant decrease was observed after 52 days for B3 level contamination, whereas C-biomass in B1 microcosms remained stable. In Au soil, the observed decrease in C-biomass of B3 microcosms occurred earlier, after 14 days, whereas no modification was observed at the B1 level of contamination. At t52d, the amount of C-biomass was once again similar to that of the control soil (Table 3).

View this table:

C-Biomass measurement for the microcosm experiment with the two soils

SoilsBiomass content
(± 14)(± 6)(± 13)(± 10)(± 8)(± 23)(± 7)(± 8)(± 4)
(± 13)(± 15)(± 6)(± 9)(± 8)(± 6)(± 11)(± 12)(± 28)
  • Data are means of three independent repetitions of plots±standard deviation.

  • Superscript letters indicate significant differences at p<0.05.

B-ARISA fingerprinting

B-ARISA fingerprinting of the bacterial community provided complex profiles with peaks ranging from 200 bp (i.e. 50 bp IGS) to 1200 bp (1050 bp IGS) for the different plots of each soil (Fig. 2a). On the basis of a previous optimization (Ranjard et al., 2003) we detected 100 bands with a resolution of 2 bp. Visual comparison of the B-ARISA profiles (Fig. 2a) showed that each soil was characterized by a specific and reproducible pattern, suggesting a particular genetic structure of the bacterial communities, as confirmed by a significant discrimination by PCA (data not shown). Furthermore, a weak influence of the spatial heterogeneity in each site was illustrated by an overlapping of the statistical areas (representing 90% confidence) drawn over the replicates of plots of Ep and Au soils at T0 m (Fig. 3). At the end of the contamination period (T2 m), PCA demonstrated a significant discrimination between control and contaminated plots of Ep soil. Control plots were significantly separated from B1 and B3 plots on both axes, which explained 43% and 22% of the total variability (Fig. 3). At T4 m and T12 m, the discrimination was not significant between the control and contaminated plots. In Au soil, a significant discrimination occurred at T4 m which was only observed between B3 and control plots, separated on the first axis which explained 27% of the total variability (Fig. 3).


ARISA profiles obtained from DNA extracted from independent replicates of control, B1 and B3 field plots before copper contamination at T0 m. (a) Bacterial-ARISA profiles, (b) Fungal-ARISA profiles.


PCA ordination of the bacterial genetic structure of replicate samples from control, B1 and B3 plots of Ep and Au field sites at each sampling date. White spots: control plots; light grey spots: B1 plots; dark grey spots: B3 plots. Statistical ellipses represent 90% confidence.

In microcosm incubation of Ep soil, the impact of Cu contamination was observed at t14d with a significant separation of contaminated (B1 and B3) samples from control samples on the first and second axes, which explained 45.7% and 19.5% of the total variability, respectively (Fig. 4). After 52 days, the ARISA profiles from the various treatments were not significantly discriminated by PCA. For Au soil microcosms, a significant impact of Cu spiking was observed at t52d on the B-ARISA profiles of B3 treatments, which is significantly discriminated from control and B1 profiles on the first axis which explained 26.3% of the variability (Fig. 4).


PCA ordination of the bacterial genetic structure of replicate microcosms from control, B1 and B3 plots of Ep and Au soils at each sampling date. White spots: control plots; light grey spots: B1 plots; dark grey spots: B3 plots. Statistical ellipses represent 90% confidence.

PCA of RISA bands allowed one to identify bands involved in the genetic structure discrimination induced by Cu treatment. Whatever the soil (Ep vs. Au) or experiment type (field vs. microcosm), modifications concerned numerous bands, as summarized in Table 4. The number of positive (bands of higher intensity in Cu treatment) and negative (bands of lower intensity in Cu treatment) variations of B-ARISA profiles were well balanced in each case where Cu had a significant impact (Table 4). Although the number of discriminating bands were similar between field and microcosm experiments, the variations in band intensity detected in profiles from field experiments were low and generally undetectable without imaging analysis software, whereas in microcosm experiments they were more marked and easily detectable.

View this table:

Comparison of B- and F-ARISA profile modifications between Cu and uncontaminated plots of Ep and Au soils. Only sampling dates exhibiting a significant discrimination between copper (B1 + B3) and control plots were considered.

Negative variationsPositive variations
Ep T2m field samples810
Ep t14d microcosm samples78
Au T4m field samples108
Au t52d microcosm samples77
Ep T2m field samples137
Ep t14d microcosm samples107
  • Positive variations represent the number of more intense RISA bands detected in copper spiked plots compared to control plots. Negative variations represent the number of less intense RISA bands detected in copper spiked plots compared to control plots.

F-ARISA fingerprinting

At the beginning of the experiment, before application of the treatments, numerous differences (i.e. bands specific to a profile or common to several profiles with different relative intensities) between the different plots of Ep and Au soils were recorded (Fig. 2b). In spite of this inter-plot variability, the PCA of the F-ARISA profiles revealed that significant modifications due to Cu contamination occurred at T2 m in Ep soil (Fig. 5). B3- and B1-contaminated plots were separated from control on the first axis, which explained 22% of the variability. At T4 m, discrimination was less robust and at T12 m the statistical areas designed over the replicates of contaminated samples overlapped with those of control samples (Fig. 5). Shifts in F-ARISA profiles were mainly due to a decrease in band intensity, as 13 negative variations were recorded vs. seven positive ones (Table 4). For Au soil, no significant modifications in F-ARISA profiles were observed during the course of the experiment.


PCA ordination of the fungal genetic structure of replicates samples from control, B1 and B3 plots of Ep and Au field site at each sampling date. Statistical area design over the plot replicates represent 90% confidence. White spots: control plots; light grey spots: B1 plots; dark grey spots: B3 plots. Statistical ellipses represent 90% confidence.

In the microcosm experiment, F-ARISA profiles appeared to be more reproducible between replicates than those observed between plots in field conditions (data not shown). In Au soil, no significant modifications occurred in F-ARISA profiles following the different treatments during the incubation period, whereas in Ep microcosms a significant discrimination was only observed for B3 contamination after 14 days of incubation which did not persist after 52 days (Fig. 6). Such discrimination was mainly due to seven positive and four negative bands which were greater in intensity than for field experiments (Table 4).


PCA ordination of the fungal genetic structure of replicates microcosms from control, B1 and B3 plots of Ep and Au soils at each sampling date. White spots: control plots, light grey spots: B1 plots, dark grey spots: B3 plots. Statistical ellipses represent 90% confidence.


Total and extractable Cu content and dynamics in soil

Cu addition in the form of ‘Bordeaux mixture’ increased the total Cu content in the 0–5 cm layer of the field plots by 20–30 mg Cu kg−1 for B1 and 50–70 mg Cu kg−1 for B3 (Fig. 1). These values indicate that the safety limits laid down by European authorities (100 mg Cu kg−1 soil) could be reached after some years of Cu use. In each plot, EDTA-extractable Cu was measured because it has been shown to correlate better with observed Cu toxicity in soil than total Cu content (Huysman et al., 1994). The ratio of Cu-EDTA to total Cu increased with the level of contamination (Fig. 1). This higher Cu extractability could be explained by the saturation of adsorption sites of metal on minerals and organic soil compounds with increasing contents of Cu in the soil.

Differences in the Cu-EDTA fraction (expressed as a part of total Cu) observed between the studied soils suggested a lower extractability of Cu in the Ep soil than in Au soil (Fig. 1). These data could be related to the higher content of clay and organic matter in Ep soil (Table 1), which are known to efficiently immobilize Cu by adsorption and/or chelating processes (Alloway, 1995; Ranjard et al., 1997; Kikkila et al., 2002). Furthermore, Fe oxyhydroxides and carbonate minerals, involved in Cu fixation (Morgan, 1988; Parat et al., 2002), were higher in Ep soil (data not shown). These soil characteristics could therefore reduce Cu mobility and explain why total and Cu-EDTA contents remained stable in Ep soil during the experiment, whereas they declined in Au soil (Fig. 1). Such a progressive reduction in Cu bioavailability could be partly explained by the ‘ageing’ process which resulted from the diffusion of Cu into inaccessible micropores in the soil matrix (Tom-Petersen et al., 2003) and/or a stronger adsorption of Cu over time, on to solid mineral and organic soil components (Hogg et al., 1993; Keller & Védy, 1994).

Impact of Cu treatment on microbial biomass

Measurement of C-biomass in field experiments showed no strong modifications induced by Cu contamination at any sampling date (Table 2). Such observations could be the result of the spatial variability leading to high standard deviation (Table 2) that could mask slight C-biomass modifications consecutive to the treatments. Significant differences occurred only between sampling dates and could result from changes in climatic conditions due to seasonal variation. In the literature, contradictory results about the impact of Cu on soil C-biomass have been recorded, illustrating the difficulty in comparing different studies with different types of contamination and soil.

In our microcosm experiments, the ‘Bordeaux mixture’ was added as a single pulse and soil was incubated in stable conditions of water content, aeration and temperature. In these conditions, only B3 contamination induced a significant decrease in the C-biomass of Ep and Au soils (Table 3) suggesting that the conventional Cu treatment used in viticulture (B1) did not quantitatively affect the soil microbial biomass. However, the decrease observed for B3 was low, close to the experimental error and different among soils. The lesser impact of Cu observed in Ep soil (biomass decreases of 6% at t52d) compared to Au soil (biomass decreases of 16% at t14d) could be related to the lower bioavailability and thus toxicity of metallic elements in Ep soil, as discussed in Total and extractable Cu content and dynamics in soil. Furthermore, the resilience of C-biomass modifications observed at t52d in Au soil confirmed the weak and transitory impact of Cu treatment.

Impact of Cu treatment on the genetic structure of microbial community according to soil type

Results obtained from ARISA fingerprinting showed that agricultural Cu contamination (from 20 to 60 mg Cu kg−1), although below the EU safety limits (100 mg Cu kg−1 soil), induced significant modifications in bacterial and fungal community structure (Figs 3,4,5,6). These modifications were resilient after a few months or weeks (in field and microcosm experiments, respectively) suggesting a transitory effect of the Cu stress that might be partly due to the progressive reduction of Cu bioavailability in soil over time as described above (see ‘Total and extraction Cu content and dynamics in soil’). Whatever the soil type and the kind of experiment (field or microcosm), the magnitude of the modifications observed for the B1 level of contamination was always lower or equal to that observed for the B3 level. In the same way, Frostegard et al. (1993) demonstrated that gradual changes in community structure fitted with the increasing Cu content in soil.

Unexpectedly, the impact of Cu on microbial communities was lower and/or delayed in Au soil compared to Ep soil (Figs 3 and 5). This trend was confirmed in the microcosm experiment (Figs 4 and 6). These results were not consistent with the evaluation of Cu extractability, which suggested a higher Cu toxicity in Au soil (see Total and extractable Cu content and dynamics in soil), emphasizing the difficulty in relating the impact of Cu on microbial diversity to the metal bioavailability and soil characteristics. Technically, this correlation could be improved by a better evaluation of Cu bioavailability, for example with the use of a biosensor (Tom-Petersen et al., 2004). Furthermore, a more detailed analysis of diversity modifications in microbial groups, which may have been masked by using universal bacterial and fungal primers, could reveal more significant shifts in Au soil (Tom-Petersen et al., 2003). As suggested by several authors, the discrepancy between estimated metal bioavailability and impact could be partly explained by the composition and the diversity of the community that might influence its sensitivity and its response to perturbation (Ranjard et al., 2000b; Girvan et al., 2004). In other words, the innately higher proportion of certain bacterial groups known to be less resistant to heavy metals, such as Gram-positive bacteria (Duxburry & Bicknell, 1983) or bacteria belonging to the Rhizobium group (Giller et al., 1998), could lead to higher shifts in community structure as the whole community response could be mainly due to the impact on this particular group (Tom-Petersen et al., 2003).

Fungal vs. bacterial modifications induced by Cu treatment

A differential Cu impact was observed within soil microorganisms characterized by a greater effect on bacterial than on fungal communities. This observation was especially marked in Au soil, with significant modifications observed in the bacterial community and none in the fungal community in field or microcosm experiments (Figs 3,4,5,6). In Ep soil, the stronger impact on the bacterial community was also evidenced by (i) the higher explained variability on the first axis which discriminates control plots from contaminated plots in field experiments (Figs 3 and 5), and by (ii) the detection of shifts in the bacterial community for the B1 level in microcosm experiments, whereas in terms of the fungal community no modifications were recorded (Figs 4 and 6). These results could be partly explained by the high variability of the ARISA fingerprinting of fungal communities that could hamper the detection of specific shifts (Ranjard et al., 2003).

Few similar studies are available in the literature which would allow us to confirm that the fungal community seems to be less sensitive to metal contamination than the bacterial one. This lack of data could be explained by the recent development of molecular tools for the analysis of the genetic structure of fungal communities (Anderson & Cairney, 2004; Bidartondo & Gardes, 2005) and by the fact that a large part of soil fungal ecology has focused on mycorrhiza. Meharg (2003) demonstrated that mycorrhiza diversity remains stable in highly contaminated soils but underlined the lack of conclusive evidence for adaptive mechanisms of tolerance. Fungi are well-known to be tolerant to metallic elements due to constitutive mechanisms such as an important intra- and/or extracellular metal immobilization capacity (Meharg, 2003). However, little is known about the genetic determinants and biochemical processes specifically involved in Cu resistance, whereas for bacteria several genetic determinants and biochemical pathways have been identified (Mergeay et al., 2003).

Field vs. microcosm evaluation of the impact of Cu

By evaluating different impacts of metal on soil microbial biomass and activity, Renella et al. (2002) showed a lesser deleterious effect in soil microcosms compared to field plots. They concluded that microcosm experiments which use acute exposure is a poor model to evaluate microbial modifications developed under conditions of chronic metal toxicity at field scale. In our study, the decrease in C-biomass due to Cu spiking was only detected in microcosm experiments, probably because of the abstraction of pedo-climatic variations that could mask some weak but significant microbial density modifications in field experiments (Table 2). These results therefore stressed that microcosm experiments could be a relevant way to estimate the ‘potential’ deleterious effects of contamination on soil microorganisms. Regarding community structure, the same trend, in terms of magnitude and resilience of modifications, was observed in field and microcosm experiments. However, though the number of discriminating bands was not higher in microcosm experiments (Table 4), the increase in band intensity for positive variations in ARISA profiles were more marked and easily detectable. In the same way, Tom-Petersen et al. (2003) observed comparable Cu effects between field and microcosm experiments through the modifications within the Rhizobium–Agrobacterium group, but in addition, they detected an impact within the Cytophaga group undetectable in field samples. All these results suggested that short-term experiments, involving an acute exposure to Cu and stable laboratory incubation conditions, could result in a more precise evaluation of the potential impact of metal on soil microbial communities. Similarly, Eller et al. (2005) demonstrated that the extrapolation of microcosm data, in terms of community structure and development, is possible. However, the reliability of this extrapolation is dependent on the scale and the artificiality of the microcosms, and the outcomes are predetermined by the investigator's choice of experimental conditions (Kamplicher et al., 2001).


We are grateful to P.A. Maron, J.C. Lata and M. Bouley for helpful discussion and comments on the manuscript. We wish to extend our sincere thanks to V. Faloya and G. Bussière who gave us access to Epoisse and Auvillars field experiments, respectively.


  • Editor: Kornelia Smalla


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