Effect of soil copper on the response of soil fungal communities to the addition of plant residues

Pedobiologia 53 (2010) 353–359

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Pedobiologia - International Journal of Soil Biology journal homepage: www.elsevier.de/pedobi

Effect of soil copper on the response of soil fungal communities to the addition of plant residues Guixin Chu a , Steven A. Wakelin b,c,∗ , Leo Condron d , Alison Stewart d a

College of Natural Resources and Environmental Sciences, Shihezi University, Xinjiang, PR China AgResearch Ltd., Lincoln Research Centre, Christchurch, New Zealand CSIRO Land and Water, Sustainable Agriculture Flagship, PMB2, Glen Osmond SA5064, Australia d Bio-Protection Research Centre, PO Box 84, Lincoln University, Lincoln 7647, Christchurch, New Zealand b c

a r t i c l e

i n f o

Article history: Received 20 December 2009 Received in revised form 12 April 2010 Accepted 28 April 2010

a b s t r a c t Organic residues provide the fundamental energy supply supporting soil fungal communities. Provision of adequate energy is required for soil microbial communities to adapt and function in the presence of ecological stress, such as copper (Cu) contamination. However, contamination can also lead to decreased ecological fitness of microorganisms, limiting their ability to access substrates. Thus, complex interactions exist between substrates, metals, energy supply/accessibility, fungal communities and their processes, and these have implications for ecosystem processes. We investigated the interaction between energy resources and Cu tolerance on soil fungal communities, including Fusarium and Trichoderma (model disease causing and beneficial genera). Using quantitative PCR and DGGE fingerprinting, the effects of increasing soil Cu levels (0 to >3000 mg Cu kg−1 soil) on size and structure of soil fungal communities were tested under basal and plant-residue (medic; Medicago trunculata) added conditions. The interaction between increasing soil Cu levels and the addition of plant resources on fungal community structure was tested using multivariate analysis. The relative size (DNA copies per unit of soil DNA) of soil fungal communities, including Trichoderma and Fusarium, significantly (P < 0.05) increased (94% and 32% respectively) with addition of medic to soil. In medic-applied samples, the bacterial to fungal ratio decreased, demonstrating the selective influence of the cellulose-rich substrate on the fungal community. Under the high nutrient conditions fungal DNA increased as a fraction of the total soil DNA, demonstrating the tolerance of fungi to Cu (relative to other microbiota) given adequate energy resources. Copper had no impact on the abundance of Fusarium or Trichoderma, but significantly affected community structure (PERMANOVA; P < 0.05). With increasing Cu, species selection and replacement could be observed, particularly in soils where medic had been included. Plant residue addition itself was a highly selective factor affecting the structure of communities of Trichoderma and Fusarium (P < 0.05). The effects of increasing Cu could be seen in both medic and basal soils for Trichoderma, but only in the basal treatments for soil Fusarium. This was due to very low dispersion in Fusarium community structure in the medic-added treatment (PERMDISP; P < 0.05). The results show the interactive influence of organic matter inputs and heavy metal contamination on size and structure of soil fungal communities. The data show that species selection and replacement is an important mechanism for community adaptation to increasing levels of soil Cu, and this mechanism can be influenced by addition of resources to the soil. © 2010 Elsevier GmbH. All rights reserved.

Introduction Organic residues in soil provide the fundamental energy supply supporting soil fungal communities and their biogeochemical processes associated with soil nutrient cycling, soil fertility and plant health (Garrett 1963). Soil disturbance events (ecological stress)

∗ Corresponding author at: AgResearch Ltd., Lincoln Research Christchurch, New Zealand. Tel.: +64 3 3259981. E-mail address: [email protected] (S.A. Wakelin). 0031-4056/$ – see front matter © 2010 Elsevier GmbH. All rights reserved. doi:10.1016/j.pedobi.2010.04.002

Centre,

affecting the supply of substrate to soil fungi or the fitness of fungal communities to access substrates may impair the ability of soils to adequately to function (e.g. Wakelin et al. 2010b). This may have profound effects on the ability of the ecosystem to function (resilience and resistance). Furthermore, exposure of soils to persistent stress, such as occurring by contamination with metals or organic contaminants, can cause shifts in the soil fugal community (Gadd 1993; Wakelin et al. 2010b) and, therefore, have implications for fungal mediated ecosystem processes. Copper (Cu) functions as a co-factor for various metalloenzymes and, as such, is an essential trace element. Over threshold

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concentrations, however, Cu exerts strong cellular toxicity due to interference with normal enzyme functioning (Gadd et al. 2001). This can occur through Cu-mediated oxidisation of macromolecules (particularly proteins and nucleic acids) and formation of hydroxide radicals through site-specific Fenton mechanisms (Gerba 2009). As Cu has high toxicity to microorganisms relative to plant and animal life, it is widely used as an antimicrobial agent. Formulations of Cu salts, such as CuSO4 , have been used for the control of microorganisms causing plant diseases for well over a century. Various formulations are still widely used to prevent and control oomycete, fungal and bacterial pathogens on numerous horticultural and ornamental plant species. In farming systems based on organic principles, or where integrated pest management is employed, Cu is often heavily relied upon as a fungicide or general pesticide. When used intensively, Cu can accumulate in soil to undesirable levels. Thus, the extensive use of Cu-based pesticides has led to concern about the impact of this metal on soil microorganism and associated ecosystem processes (Giller et al. 1998). Even a single application of Cu to soil has been shown to have long-term impacts on microbial community structure and function (Wakelin et al., 2010a). However, the sensitivity of key groups of soil bacteria to Cu varies widely. For example, Acidobacteria were found to be very susceptible to increasing Cu but Bacillus very tolerant (Wakelin et al., 2010a). Yet little is known of the response of soil fungi; this is despite their fundamental importance in soil geochemical cycling (including fertility) and plant health (Babich and Stotzky 1985). Various intrinsic traits can affect the sensitivity of soil fungi to metals. These include the inherent ability of fungi to produce metal binding proteins, exude ligands to result in organic and inorganic precipitation, to transport or compartmentalise metals from cytoplasm exposure, and even reduce Cu2+ (reviewed by Gadd 1993). As these mechanisms are active, they are reliant on the nutritional status of the fungus (Gadd et al. 2001). Stress factors affecting limitation of fungal growth, particularly nutrition, might therefore be expected to influence the ability of fungi to tolerate soil metals. In this study, we explore the effects of Cu, over a dose–response range, with and without plant residue addition, on the abundance and diversity of the total soil fungi community and also the important genera Fusarium and Trichoderma. These two genera represent key groups of ubiquitous soil fungi involved in soil nutrient cycling, particularly decomposition of plant matter and the associated transfer of nutrient resources into the microbial pool. In addition, soil Fusarium species are some of the most common soil pathogens of plants, whereas Trichoderma species are regarded as highly beneficial. Thus, soil conditions favouring growth of one group over the other may have direct implications for plant health as well as biogeochemical cycling and ecosystem function.

Methods and materials Microcosm experiment A microcosm experiment was conducted using soil from Spalding, South Australia, in which a Cu-gradient from 56 to 3225 mg Cu kg−1 soil had been established some 6 years previously (Broos et al. 2007; Wakelin et al. 2010b). The soil pH is 6.3 (0.01 M CaCl2 ), CEC 18 cmolc kg−1 , organic C and clay content 1.9 and 24% respectively (Broos et al. 2007). Field soil was collected from 12 field micro plots, dried, sieved to 2 mm and stored at 4 ◦ C until use. Soils from each of the 12 Cu applications levels were incubated with and without addition of plant material. Each microcosm consisted of a 1 L incubation jar in which 18 g soil had been added into a plastic container at a uniform bulk density of 1.5 g soil cm−3 . For substrate-added samples, dried and ground barrel medic (Med-

icago trunculata; C = 39.8%, N = 4.09%) was pre-mixed into the soil at 3% (by weight); ‘basal’ treatments received no additional plant material. Soils were adjusted to 60% WHC and a small receptacle of water was included in each incubation jar to maintain humidity. Microcosms were incubated at 25 ◦ C for 20 d in the dark. At the conclusion of the incubation, DNA was extracted from samples of soils using the PowerSoil DNA extraction kit (MoBio Inc., CA) and quantified using PicoGreen dsDNA quantification method (Invitrogen) on a Stratagene MX3000P qPCR system (Agilent Technologies Inc., CA). Full details of the soil type, Cu range, incubation experiment methodology and DNA extraction and processing are given elsewhere (Broos et al., 2007; Wakelin et al. 2010b). Quantification of soil microbial communities Soil bacterial and fungal communities were quantified using real time PCR (qPCR) based on the abundance of their respective SSU rRNA genes. For soil bacteria, the primers BACT1369F and PROK1492R were used to amplify a region of the 16S rRNA gene and quantification was based on 5 nuclease activity of an internal ‘taqman’ probe (TM1389F labelled with FAM and BHQ-1), as described by Suzuki et al. (2000). In the PCR, primers were at 1.5 ␮M and 2.5 ␮M (respectively), the probe at 0.5 ␮M, and dNTP’s at 10 mM each. Taq (HotStar TaqPlus; Qiagen) was used at 1.2 U per reaction; the total reaction volume was 25 ␮L and included 2 ␮L of the soil community DNA. PCR followed a 2-step cycle; 95 ◦ C for 15 s and 56 ◦ C for 1 min. For the soil fungal community, primers FR1 and FF390 were used to amplify a fragment of the 18S rRNA as described by Vainio and Hantula (2000). Primers were used at 0.4 ␮M each in a complete SYBR-green PCR reaction chemistry (Qiagen QuantiTect reaction mix) with 2 ␮L of target DNA. After hot-start enzyme activation, reaction cycles consisted of 95 ◦ C for 30 s, 50 ◦ C for 45 s and 72 ◦ C for 2 min. Quantification of soil Trichoderma fungi was based on amplification of a region of the ITS-rRNA gene with primers T230F and T397R (Liu et al. 2008). The primers were used at 2 ␮M each in a complete SYBR-green PCR reaction chemistry mix (as before). After hot-start enzyme activation, the PCR cycled between 94 ◦ C 1 min, 60 ◦ C 1 min and 72 ◦ C for 3 min. Reactions used 2 ␮L of target DNA in a total volume of 25 ␮L. Quantification of Fusarium in soil was based on a nested PCR approach targeting the elongation factor (EF1␣) gene (Yergeau et al. 2005). In first-round PCR, non-specific amplification of the EF1␣ gene was conducted using primers EF1 and EF2 (O’Donnell et al. 1998) and amplification was monitored with SYBR-based detection. When all reactions were in exponential amplification, thermocycling was stopped and 2 ␮L of a 1/100 dilution of the reaction mix used as template for 2nd round, Fusarium-specific PCR with primers Alfie 1F and Alfie 2R (Yergeau et al. 2005). Details of this semi-quantitative method, including PCR chemistry used, are given in Wakelin et al. (2008). Ultimately, each difference in CT was inferred to equal a doubling in difference in the size of the Fusarium community (with respect to amplification efficiency), and this difference was calculated over the lowest value in the experiment (given a nominal value of 1). As the lowest values correspond to the highest Fusarium EF1␣ gene copies, the difference to the maximum value was then calculated. For example, a value of 43 equates to 43 × number of Fusarium EF1␣ gene copies than present in the lowest sample. All qPCRs were conducted on a Stratagene MX3000P machine (Agilent Technologies). Standards for quantification were generated by cloning a positive gene-fragment of interest (e.g. 16S rRNA gene) into pGEMT vector system (Promega). Dilution series were generated from bulk plasmid preps and quantified using PicoGreen spectroscopy (as before). With the exception of the Fusarium results, data are expressed as copy numbers of target

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genes per ng of DNA. Agarose gel electrophoresis (1.5% in TAE) was used to confirm the presence of amplicons of the expected size. To confirm PCR specificity, reaction mixtures from Cu rate 1 and Cu rate 8 from each of the PCR’s were randomly cloned into pGEMT vector (Promega) and resultant inserts sequenced from the M13 region (Australian Genome Research Facility, Adelaide). From the fungi-specific PCRs, 80 sequences were generated, from the Trichoderma specific PCR 48 sequences, and from the Fusarium-specific PCR 92 sequences. Community-level fingerprinting of Trichoderma and Fusarium Community-level fingerprinting of soil Trichoderma and Fusarium was based on denaturing gradient gel electrophoresis (DGGE) genotyping of the respective ITS-rRNA and EF1␣ genes. PCR was used to specifically target genera of Trichoderma and Fusarium using conditions only slightly modified from those described for qPCR. To avoid heteroduplex formation, GC-rich clamps were added to primers T230F and Alfie1F (Muyzer et al. 1993). PCR chemistry was based on HotStar Taq (Qiagen); DNA was added at 2 ␮L per reaction, dNTPs were added to the reaction mix at 10 mM each, polymerase enzyme used 1.2 U per 25 ␮L reaction. Thermocycling used conditions described before, except 30 cycles were used for Trichoderma PCR, and for both steps of the Fusarium PCR. At conclusion of the amplification steps, PCRs were held at 72 ◦ C for 20 min to further reduce double banding artefacts (Janse et al. 2004) and then held at 4 ◦ C until use. DGGE separation of PCR reaction mixtures was performed in an Ingeny PhorU system. Profiling of Trichoderma ITS-rRNA genes used an 8% acrylamide:bis-acrylamide (37.5:1) gel with a formamide:urea denaturing range of 25–65%. For separation of Fusarium EF1␣ genes, 6% gels and a 40–60% denaturing range was used. Electrophoresis was conducted at 60 ◦ C with 110 V over 17 h. Gels were stained in SYBR gold (1× in TAE buffer; Molecular Probes) for 30 min and visualized on a DarkReader (Clare Chemicals Inc., USA). Gel images were digitally captured using an Olympus E-500 digital SLR camera and the position and intensity of bands were determined using Gel-Quant software (Multiplexed Biotechnologies Inc). Band intensity data from the DGGE gels were square-root transformed and the similarity between samples determined using the Bray–Curtis coefficient; this similarity measure has been shown to have general suitability for ecological studies (Clarke and Warwick 2001; Clarke et al. 2006). Distances between samples were interpreted by non-metric multidimensional scaling (nmMDS). The effects of Cu on the structure of the communities of Trichoderma and Fusarium in the presence and absence of medic substrate were tested using permutation-based multivariate analysis of variance (PERMANOVA+ for PRIMER; Anderson et al. 2008) using approaches described in Anderson (2001). In the design, medic addition was used as a fixed treatment and Cu levels were treated as a covariate with interaction with medic. The PER-

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MANOVA design and analysis parameters have been fully described previously (Wakelin et al. 2010b). Microscopy Soil aggregates were collected from the medic added, Cu 1 and Cu 8 treatments at the end of the incubation study. Soil was shaken loose from the containers onto waxed paper and aggregates collected into sterile plastic containers. The aggregates were fixed in a solution of 4% paraformaldehyde, 1.25% glutaraldehyde in phosphate buffered saline (PBS; pH 7) + 4% sucrose for 20 mins, washed in PBS, step-dehydrated in ethanol and dried in a Bal-tec CPD 030 critical point dryer. Samples were mounted onto stubs and high-resolution sputter-coated with Pt (Cressington 208 model). Microscopy was conducted on a Phillips XL30 SEM at the Adelaide Microscopy Centre. Results Microbial community size Addition of medic to soils strongly influenced the abundance of all microbial communities quantified (P < 0.05; Table 1 and Fig. 1). The soil total fungal community, Trichoderma community and Fusarium community all increased in relative abundance (P < 0.05); largest increases were found in the Trichoderma (94% increase) and total soil fungal communities (73% increase), but the increase in relative abundance of Fusarium was still strong (32%) (Table 1). The response of both Trichoderma and Fusarium fungi to addition of medic to soil was not influenced by soil Cu (P > 0.05; Table 1 and Fig. 1). Cu did, however, significantly affect the relative abundance of the total soil fungal community (Table 1 and Fig. 1). With increasing Cu, the abundance of 18S rRNA genes (per unit of soil DNA) increased, but this effect was only measured in samples where medic was added. Although the response was strong (R2 = 0.505), it was influenced by two high values at the highest Cu concentrations (Fig. 1). The addition of medic to soil significantly decreased the bacterial to fungal ratio from an average of 16:1 in the basal soil, to 7:1 in the medic added soil (P < 0.05; Table 1 and Fig. 1). Where medic was added, there was no effect of Cu on the bacterial to fungal ratio. However, the microbial community became increasingly fungal-dominated with increasing Cu in the basal soil (Fig. 1 and Table 1). Microscopy showed confluent growth of fungi and bacteria (Fig. 4) on soil aggregates at low Cu levels. To some extent, this was observed in the SEM micrographs of soil aggregates (Fig. 4). At high Cu levels, total observations of both fungi and bacteria decreased, but cells of both could still be found with relative ease (Fig. 4). The addition of medic to soil decreased the ratio of total fungi to both Trichoderma and Fusarium (P < 0.05; Table 1 and Fig. 1), and the ratio of Trichoderma to Fusarium increased (P < 0.05). The presence of Cu in soil increased the total fungi to Trichoderma ratio (Table 1

Table 1 Summary table of results of 1-way ANOVA analysis of soil DNA qPCR data with medic addition as the major factor and Cu levels as a covariate. Fungal community

bacteria: fungi ratio

Trichoderma community

total fungi: Trichoderma ratio

Fusarium community

total fungi: Fusarium ratio

Trichoderma: Fusarium ratio

P

Medic Cu (covariate)

<0.001 0.011

<.001 0.012

<0.001 0.454

0.006 0.037

<.001 0.372

0.341 0.313

0.002 0.437

¯ X

Basal Medic added

389923 1418300

15.8 6.8

42067 633333

11.8 3.0

512 749

45303 1904

84 841

Fungal and bacterial community abundance determined from 18S and 16S rRNA gene numbers, respectively. Trichoderma community quantified from Trichoderma-specific ITS-rRNA gene fragment. Fusarium community quantification based on transcription elongation factor (Ef1␣) gene abundance, using semi-quantitative qPCR (relative change in gene copies between samples).

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Fig. 1. Plots of abundance responses of soil microbial communities to the addition of medic to soil over increasing range of Cu. Regression lines given when significant (P < 0.05) fits occurred.

and Fig. 1), but in basal samples only. No significant linear correlation was found between Cu and the total fungi to Trichoderma ratio. Results of random sequencing of PCR products showed 100% specificity for the general fungal PCR, 96% specificity for the Trichoderma primers (2 sequences returned matches for Fusarium spp.) and 100% specificity for the Fusarium-specific PCR primers. Fungal community structure The community structure of Fusarium was significantly affected by medic addition (P < 0.002; Table 2). In the basal soil microcosms, a very strong effect of Cu on Fusarium community structure was

seen (Fig. 2 and Table 2). However, with addition of medic the effects of Cu were greatly diminished (Fig. 2). The strong selective pressure that medic addition had on the community structure was highlighted by the much lower dispersion in community similarity between basal (36.85) and medic added (16.42) samples (P = 0.001; Table 2 and Fig. 2). Similarly, the species composition of Trichoderma in soil was significantly (P = 0.001) affected by addition of plant residue to the soil (Table 2 and Fig. 3). Clear separation of community type with medic addition is evident in the nmMDS ordination plot (Fig. 3), and a gradient effect of Cu for both the basal and medic community types can be seen (P < 0.05; Fig. 3). In contrast to results for the

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Table 2 Summary of analysis of effects of medic addition over a Cu contamination range on fungal, Trichoderma, and Fusarium community structure and dispersion. √ CV ( ) Fusarium PERMANOVA

Medic addition Cu Cu × medic Residual

18.86 9.91 15.07 26.86 X¯

Pseudo-F 4.27 6.91 4.78

S.E.

P 0.002 0.001 0.001

P

Fusarium PERMDISP

Basal Medic added

36.85 16.42 √ CV ( )

2.88 1.59

0.001

Pseudo-F

P

17.41 13.16 9.28 21.37

10.10 8.96 3.26

0.001 0.001 0.009

X¯

S.E.

P

Trichoderma PERMANOVA

Medic addition Cu Cu × medic Residual

Trichoderma PERMDISP

Basal Medic added

25.59 22.44

2.61 1.82

0.436

Community structures were compared using permutation-based multivariate analysis of variation (PERMANOVA) with medic as a fixed factor and Cu as a covariate. P values derived against a permutation (999) generated null distribution under a √ reduced model. CV( ) is the square root of the component of variation (Anderson et al. 2008), a measure of the effect size in units of the community dissimilarities. Average dispersion in community structure (PERMDISP) over the copper range in the basal and medic added treatments was tested from group centriods; P was calculated by permutation (as above).

soil Fusarium community, the impact of Cu on the dispersion of Trichoderma community type was similar (P = 0.436) for both the medic and basal soils treatment levels (Fig. 3 and Table 2).

Fig. 2. Fusarium PCR-DGGE and associated nmMDS plot. Bands (Ef1␣ genotype) on the DGGE are inferred to represent a single taxonomic unit. Band intensity data (abundance of the taxonomic unit) was square-root transformed to down-weight importance relative to band presence. Similarity in community structure between samples was calculated with the Bray–Curtis method. nmMDS ordination was based on 2500 iterations. Increasing distance between points on the nmMDS equate to increasing dissimilarity in community structure between samples.

Discussion The addition of plant residue to soil significantly increased the abundance of soil fungi, irrespective of soil Cu level. Given that the community had tolerance to high rates of Cu, adaptation to the presence of Cu in soil was evident. This was supported by previous data showing continuation (but reduction) of medic decomposition at high Cu rates (>3000 mg Cu kg−1 soil) in soils after exposed to Cu for over 5 years (Wakelin et al. 2010b). The emergence of Cu tolerance was driven by an alteration in microbial community structure; i.e. species selection and replacement. This mechanism of community adaptation has been shown to be an important factor shaping fungi community structure in metal rich environments previously (reviews by Babich and Stotzky 1985; Gadd 1993). From a functional perspective, links between community-level shifts, metal tolerance and biogeochemical cycling have been established. For example, Mertens et al. (2009) showed that restoration of nitrification in a zinc (Zn) contaminated soil was due to development of a Zn-adapted bacterial community structure, not from increased tolerance of species present in the low Zn soil. These results highlight the value of community-level investigations for understanding mechanisms underpinning stress/disturbance response processes by soil microbiota (Wakelin et al. 2010a). Addition of medic increased the relative abundance of fungal 18S rRNA genes in the soil DNA pool and decreased the bacterial to fungal ratio. This is consistent with the primarily role of soil fungi in degradation of plant matter with high cellulose content (Garrett 1963). Cu did not affect the abundance of fungi under basal soil conditions, however when medic was incorporated into the soil

the abundance of 18S rRNA genes increased with increasing Cu. Although this may seem counter intuitive, it should be remembered that the qPCR data presented is based on relative gene abundance (i.e. copies of target gene per unit of DNA). Thus, the results are showing that the fungal community was comparatively more tolerant to the addition of Cu to soil than other fractions of the soil biota (bacteria etc). As has been noted previously, toxicological impacts of metals on soil microbiota are often only evident under conditions of high activity, such as that provided by resource addition (Broos et al. 2005; Wakelin et al. 2010a). The increase in the relative capacity for the fungal community to tolerate Cu in the medic-added systems was likely to be due to the provision of adequate resources that favoured fungal-driven degradation. These resources are required to provide energy and growth/species selective opportunity to drive shifts in community structure towards Cu-tolerant species. However, the link between nutrition and Cu tolerance may also occur at species level. Gadd et al. (2001) explored the relationship between nutrition status and Cu tolerance in Trichoderma and Rhizopus. Under low substrate conditions, Cu and other metals caused a significant reduction in fungal growth, but this effect progressively diminished as the nutritional status of the environment increased. At a species level, the interaction between nutrition and metal tolerance is most likely to be due to provision of the energy required to actively detoxify metals. These include the expression of enzymes and ligands that can complex with Cu, mechanisms that reduce transport of Cu into the cell, that compartmentalise Cu intracellularly or actively efflux the metal (Gadd 1993). In addition, significant levels of biosorption of

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Fig. 3. Trichoderma PCR-DGGE and associated nmMDS plot. Bands (ITS rRNA genotypes) on the DGGE are inferred to represent a single taxonomic unit. Band intensity data (abundance of the taxonomic unit) was square-root transformed to down-weight importance relative to band presence. Similarity in community structure between samples was calculated with the Bray–Curtis method. nmMDS ordination was based on 2500 iterations. Increasing distance between points on the nmMDS equate to increasing dissimilarity in community structure between samples. A = Genotypes that increased with addition of medic, B = genotypes that decreased with addition of medic, C = genotypes that increased with addition of Cu, D = genotypes that decreased with addition of Cu.

Cu to the cell wall of some fungi – including Trichoderma – may reduce exposure to the cytoplasm and risk of toxicity (Anand et al. 2006). The addition of medic to soil enriched the microbial community with both Trichoderma and Fusarium fungi. This effect was irrespective of soil Cu levels, demonstrating the strong stimulatory effect of plant litter addition to these fungi. Not surprisingly, both taxa are characterised by their strong cellulolytic activity and high competitive saprophytic activity (Garrett 1963). The effects of plant residue addition on increasing the community size of Fusarium and Trichoderma have been demonstrated (e.g. Wakelin et al. 2008) but effects relative to the total microbial community are relatively unknown. Although soil Cu did not affect total abundance of these fungi, it was a key factor shaping community structure. For the Trichoderma community, DGGE revealed distinct changes in the occurrence and abundance of taxa over the range of Cu and also with addition of medic—examples of affected genotypes are labelled in Fig. 3. The driving force of Cu on altering the Trichoderma community was of similar magnitude for both the medic-added and basal treatments, as shown by the analysis of community dispersion (PERMDISP). However, for the Fusarium community structure, the effect of medic

Fig. 4. SEM images of soil aggregates and medic residue from soil Cu rate 1 and Cu rate 8. Fungal hyphae and bacterial cells were observed on the surface of soil aggregates at both copper rates, however total observations (qualitative) of microbiota were much reduced at higher Cu levels.

addition was much stronger than Cu; in medic-added soils, Fusarium community structures were similar regardless of Cu level (low dispersion). In the basal treatment, however, Cu affected a high level of dispersion. These results support previous findings that show that the structure of soil Fusarium communities is strongly affected by plant residue management compared with other factors, such as N addition (Wakelin et al. 2008). Trichoderma fungi are widely recognised as being beneficial for plant health and strains have been successfully used for this purpose (Stewart et al. 2007). There has been much interest in selecting strains for compatibility with Cu-based pesticides to increase plant disease control efficacy or extending product shelf life (e.g. Kolombet et al. 2008; Hanada et al. 2009). While isolates of Trichoderma with very high Cu tolerance have been found from environmental studies (Errasquín and Vázquez 2003; Costa et al. 2006), these sites often do not represent habitats in which biocontrol isolates might be expected to have strong niche-adaptation. Agricultural field soils in which Cu has been deliberately applied, such as the Spalding site used in this study, offer new grounds for isolation of Cu-tolerant strains, as distinct shifts in community composition have occurred after only a few years of environmental exposure to soil Cu. Metals, including Cu, are widely recognised as important drivers shaping fungal community structure (Gadd 1993). As many of these studies have been conducted in mine spoil sites or where industrial waste has entered the environment (Nordgren et al. 1983) they

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represent environments low in organic matter. The results demonstrate the importance of soil organic matter in driving community adaptation to Cu in soils, and this may represent an important mechanism for restoration of soil microbial function during rehabilitation of metal contaminated sites. Acknowledgements We appreciate the assistance by Ms. Adrienne Gregg in conducting aspects of the qPCR and DGGE and Dr. Kris Broos for help with setting up the incubation experiment. Ms. Lyn Waterhouse from the Adelaide Microscopy Centre assisted with SEM and sample preparation. Prof. Bob Clarke and Prof. Marti Anderson provided guidance with multivariate statistical analysis, in particular PERMANOVA design and testing. S.A. Wakelin was supported by a CSIRO Julius Award and the Bio-Protection Research Centre, Lincoln University. G.X. Chu visited CSIRO Adelaide with support of the China Scholarships Council. Soils for this study originated from the National Biosolid Research Programme (NBRP), Australia. Drs. Frank Reith (the University of Adelaide), Richard Lardner (ESR New Zealand) and Lynne Macdonald (CSIRO) kindly provided reviewed this manuscript pre-submission. References Anand, P., Isar, J., Saran, S., Saxena, R.K., 2006. Bioaccumulation of copper by Trichoderma viride. Bioresour. Technol. 97, 1018–1025. Anderson, M.J., 2001. A new method for non-parametric multivariate analysis of variance. Aust. Ecol. 26, 32–46. Anderson, M.J., Gorley, R.N., Clarke, K.R., 2008. PERMANOVA+ for PRIMER: Guide to Software and Statistical Methods. PRIMER-E, Plymouth, UK. Babich, H., Stotzky, G., 1985. Heavy metal toxicity to microbe-mediated ecologic processes: a review and potential application to regulatory policies. Environ. Res. 36, 111–137. Broos, K., Mertens, J., Smolders, E., 2005. Toxicity of heavy metals in soil assessed with various soil microbial and plant growth assays: a comparative study. Environ. Toxicol. Chem. 24, 634–640. Broos, K., Warne, M.S., Heemsbergen, D.A., Stevens, D., Barnes, M.B., Correll, R.L., McLaughlin, M.J., 2007. Soil factors controlling the toxicity of copper and zinc to microbial processes in Australian soils. Environ. Toxicol. Chem. 26, 583–590. Clarke, K.R., Warwick, R.M., 2001. Change in Marine Communities: An Approach to Statistical Analysis and Interpretation, 2nd ed. PRIMER-E, Plymouth, UK. Clarke, K.R., Somerfield, P.J., Chapman, M.G., 2006. On resemblance measures for ecological studies, including taxonomic dissimilarities and a zero-adjusted Bray–Curtis coefficient for denuded assemblages. J. Exp. Mar. Biol. Ecol. 330, 55–80. Costa, I.P.M.W., Cavalcanti, M.A.Q., Fenandes, M.J.S., Lima, D.M.M., 2006. Hyphomycetes from soil of an area affected by copper mining activities in the state of Bahia. Brazil. Braz. J. Microbiol. 37, 290–295. Errasquín, E.L., Vázquez, C., 2003. Tolerance and uptake of heavy metals by Trichoderma atroviride isolated from sludge. Chemosphere 50, 137–143.

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