A single application of Cu to field soil has long-term effects on bacterial community structure, diversity, and soil processes

ARTICLE IN PRESS Pedobiologia 53 (2010) 149–158

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Pedobiologia journal homepage: www.elsevier.de/pedobi

A single application of Cu to field soil has long-term effects on bacterial community structure, diversity, and soil processes Steven Alan Wakelin a,n, Guixin Chu a,b, Richard Lardner d,1, Yongchao Liang b,c, Mike McLaughlin a,e a

Centre for Environmental Contaminants Research, CSIRO Land and Water, PMB 2, Glen Osmond, SA 5064, Australia College of Natural Resources and Environmental Sciences, Shihezi University, Xinjiang, PR China c Chinese Academy of Agricultural Sciences, Institute of Natural Resources and Regional Planning, Beijing, PR China d CSIRO Entomology, PMB 2, Glen Osmond, SA 5064, Australia e School of Earth and Environmental Sciences, The University of Adelaide, PMB 1, Glen Osmond, SA 5064, Australia b

a r t i c l e in fo

abstract

Article history: Received 24 August 2009 Received in revised form 8 September 2009 Accepted 8 September 2009

Long-term diversity-disturbance responses of soil bacterial communities to copper were determined from field-soils (Spalding; South Australia) exposed to Cu in doses ranging from 0 through to 4012 mg Cu kg 1 soil. Nearly 6 years after application of Cu, the structure of the total bacterial community showed change over the Cu gradient (PCR-DGGE profiling). 16S rRNA clone libraries, generated from unexposed and exposed (1003 mg Cu added kg 1 soil) treatments, had significantly different taxa composition. In particular, Acidobacteria were abundant in unexposed soil but were nearly absent from the Cu-exposed sample (P o0.05), which was dominated by Firmicute bacteria (P o0.05). Analysis of community profiles of Acidobacteria, Bacillus, Pseudomonas and Sphingomonas showed significant changes in structural composition with increasing soil Cu. The diversity (Simpsons index) of the Acidobacteria community was more sensitive to increasing concentrations of CaClextractable soil Cu (CuExt) than other groups, with decline in diversity occurring at 0.13 CuExt mg kg 1 soil. In contrast, diversity in the Bacillus community increased until 10.4 CuExt mg kg 1 soil, showing that this group was 2 orders of magnitude more resistant to Cu than Acidobacteria. Sphingomonas was the most resistant to Cu; however, this group along with Pseudomonas represented only a small percentage of total soil bacteria. Changes in bacterial community structure, but not diversity, were concomitant with a decrease in catabolic function (BioLog). Reduction in function followed a doseresponse pattern with CuExt levels (R2 =0.86). The EC50 for functional loss was 0.21 CuExt mg kg 1 soil, which coincided with loss of Acidobacteria diversity. The microbial responses were confirmed as being due to Cu and not shifts in soil pH (from use of CuSO4) as parallel Zn-based field plots (ZnSO4) were dissimilar. Changes in the diversity of most bacterial groups with soil Cu followed a unimodal response – i.e. diversity initially increased with Cu addition until a critical value was reached, whereupon it sharply decreased. These responses are indicative of the intermediate-disturbance-hypothesis, a macroecological theory that has not been widely tested in environmental microbial ecosystems. Crown Copyright & 2009 Published by Elsevier GmbH. All rights reserved.

Keywords: Bacteria Copper Community structure

Introduction The presence of Cu in soil can quantitatively and qualitatively affect soil microorganisms and microbial activities. Given the primary importance of microbially driven functions (particularly C and N cycling) on wider ecosystem services and soil fertility (Coleman and Whitman 2005), the effects of Cu and numerous other heavy metals on soil microbiology have been the subject of ˚ much research (see reviews by Ba˚ ath 1989 and Giller et al. 1998).

n

Corresponding author. E-mail address: [email protected] (S.A. Wakelin). 1 Currently at: Institute of Environmental Science and Research Ltd., 34 Kenepuru Drive, P.O. Box 50-348, Porirua, New Zealand.

Despite this, our understanding of the impacts that metals, including Cu, may have on soil microbiology at a communitylevel represents a major knowledge gap (Griffiths et al. 1997). However, an understanding of community-level ecology is clearly important when exploring links between biological elements and associated functions in soil ecosystems. Community-level interactions are also relevant when characterising the nature of ecosystem stability and recovery following stress disturbance (resistance and resilience, sensu Orwin and Wardle 2004). For example, the microbial community structure was shown to be a key component of functional resilience in soil ecosystems exposed to Cu (Griffiths et al. 2008). Direct links between diversity (taxa richness or evenness) and stability in macroecological systems are also well established (Tilman and Downing 1994; Naeem and Li 1997). Although the

0031-4056/$ - see front matter Crown Copyright & 2009 Published by Elsevier GmbH. All rights reserved. doi:10.1016/j.pedobi.2009.09.002

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overall importance of microbial diversity to the function of soil ecosystems is evident (Coleman and Whitman 2005), the nature of these associations and the integration of diversity with stability are less clear. This is likely due to a number of factors including a combination of (1) the very large pool of microbial species present in any given soil, (2) complementarity of functions across species, and (3) the historical inadequacy of appropriate tools to measure microbial diversity in complex ecosystems (Ogunseitan 2005). Furthermore, diversity within microbial communities may also be important for developing soil tolerance (Rutgers 2008) and/or functional recovery following the addition of metal (‘insurance hypothesis’; Tobor-Kaplon et al. 2005). This may occur by providing a diverse taxonomic base against which species selection and replacement can occur. The processes, theories, and ecological implications surrounding these have recently been the focus of review (e.g. Botton et al. 2006). Given the potential importance of diversity across a wide range of aspects linking microbial ecology and ecosystem function, it is surprising that diversity-disturbance relationships have not been investigated more widely (Horner-Devine et al. 2004). Previous studies have included systems testing relationships between soil bacterial diversity and the addition of metals. The approaches taken have varied widely and have included culture-based and culture-independent methods, laboratory to field studies and physiological profiling (Giller et al. 1998). At a community level, Cu has generally been shown to have a deleterious effect on soil microbial diversity (Griffiths et al. 1997) and physiology (Knight ˚ et al. 1997; Ba˚ ath et al. 1998; Ellis et al. 2001). Although such studies have identified relationships between metal-induced disturbance and biodiversity, the results of many studies have been confounded by interactions with other factors which also affect diversity (Horner-Devine et al. 2004). In particular, these include stress from two or more metals in soil samples and the addition of metal with biosolids (sewage sludge). Furthermore, as Giller et al. (1998) state, ‘‘it is an oversimplification to assume such basic, negative relationships are common – particularly in the absence of focused studies’’. In macroecological situations, the relationship of diversity-disturbance interactions may often follow uni-modal distributions (Grime 1973; Connell 1978; Sousa 1979). Commonly termed the ‘intermediate disturbance hypothesis’ (IDH), under these relationships diversity is lowest in lowstress environments (via competitive exclusion mechanisms), increases with disturbance as strong competitors loose their advantage allowing more species to succeed, and decreases with high disturbance due to loss (extinction) of organisms. Although the IDH was initially concerned with frequency of disturbance,

more pragmatic approaches can be used to explore such diversity-disturbance relationships, such as disturbance gradients ¨ (see discussion in Zobel and Partel 2008). The response between bacterial diversity, community structure and disturbance in soil ecosystems has received little focused attention. Importantly, studies so far either investigated contamination of soil with mixtures of metals, e.g. organic-rich metal-containing biosolids, have been performed in laboratory microcosms, or have explored the short-term responses to newly added Cu in the laboratory. Furthermore, most studies have focused on specific processes or components of the microbial system such as cultivable organisms, rather than identifying community-level responses. The aims of this work were to (1) determine long-term field effects of Cu on soil microbial processes and bacterial community structure, (2) characterise the effects of Cu on aspects of soil microbiology at a community-level, and (3) test the IDH-type effects on soil bacterial communities in response to a Cu gradient in the environment.

Materials and methods Site, soil and sampling The Spalding field trial site is located on a grain cropping farm in South Australia, approximately 160 km North of Adelaide. The soil pH is 5.2 (0.01 M CaCl2), organic C 1.9%, CEC 18 cmolc kg 1, and clay content 24% (Broos et al. 2007). The agricultural system is rain-fed (i.e. non-irrigated), and has received approximately 382 mm rain per annum over the last decade (interpolated data using geo-referenced data-drill method within the SILO database; http://www.bom.gov.au/). Copper treatments to soil spanned 12 application rates: 0, 39, 59, 88, 132, 198, 297, 446, 669, 1003, 2003, and 4012 mg Cu kg 1 soil (subsequently referred to as Cu 0 to Cu 11; Table 1). Copper was applied as CuSO4 to duplicate field plots (minimum size 3 m  4 m) in April 2002, and plots were sown with cereal grain each subsequent year. The soil pH, at the time of sampling, showed a response to the CuSO4 addition, decreasing from 5.2 (no added Cu) to 4.7 (high Cu rates). For this study, field soils were re-sampled following the harvest of wheat in November 2007 – i.e. 5.5 years after the initial, single, application of Cu. Total soil Cu (CuTot) and CaCl2-extractable Cu (CuExt) (Table 1) were determined as described by Broos et al. (2007). Soils were sieved to 2 mm and were stored at 4 1C for 6 d prior to microbial investigations.

Table 1 Total and extractable Cu levels in the Spalding soil. Treatment

Cu Cu Cu Cu Cu Cu Cu Cu Cu Cu Cu Cu a b

0 1 2 3 4 5 6 7 8 9 10 11

Targeta application rate (mg Cu kg 1 soil)

0 39 59 88 132 198 297 446 669 1003 2003 4012

Total Cub (mg Cu kg

1

soil)

CaCl-extractable Cub (mg Cu kg

1

soil)

Replicate 1

Replicate 2

Replicate 1

Replicate 2

99.12 89.62 108.79 204.87 256.90 335.05 588.80 485.57 1115.10 1568.05 2776.88 2869.07

32.03 68.35 61.59 153.11 176.64 304.15 473.78 656.63 1017.48 1255.08 3447.67 3625.67

0.16 0.11 0.14 0.25 0.38 0.57 1.43 0.96 6.11 16.62 72.68 132.48

0.16 0.12 0.12 0.19 0.24 0.38 0.91 0.96 3.39 5.28 93.82 93.54

The rate at which Cu was applied to the Spalding field trial in April 2002. Total and CaCl2-extractable Cu from the field trial plots when sampled during November 2007.

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Community-level physiological profiling The capacity of the soil microbial community to catabolise a range of organic compounds was determined using BioLog EcoPlates (Garland and Mills 1991; Campbell et al. 1997; Singh et al. 2006). Each EcoPlate contains 3 replicate sets of 31 ecologically relevant C substrates (Hitzl et al. 1997). For each of the 24 soil samples, 2.6 70.1 g was weighed into a 50 mL centrifuge tube and 27 mL of phosphate-buffered saline (PBS; pH 6.8) added. Samples were shaken by end-over-end tumbling for 2 h to completely disperse soil particles. A second 1:10 dilution was made into PBS. This dilution was mixed for a further 30 min and also while loading into BioLog EcoPlates (100 mL per well). Colour development in each well was determined at the start of the experiment (T0) using a BioLog microstation system (OD590), and then at 24 h intervals over a 4 d incubation (24 1C, dark). OD590 values for the control (water) samples were subtracted from the value for each substrate-containing well. At each sampling time, the well colour development was calculated relative to the T0 values and the average value for each substrate calculated within each plate. Finally, a total plate average for colour development was determined ( = AWCD values; Garland and Mills 1991). Dose-response curves were fitted to the data to model the effects of soil Cu on AWCD. Concentrations of CuTot and CuExt were determined by subtracting the background concentrations in the control treatment. Control samples (zero added Cu) were given nominal values to allow for log transformation. Concentration of log(Cu) versus AWCD curves were fitted using the least-squares method. EC50 values, i.e. the concentration of Cu which caused a 50% reduction in AWCD, were calculated along with 95% confidence intervals. Curve fitting and analysis was performed using GraphPad Prism version 5 (GraphPad Software, San Diego). The pattern of C-substrate utilisation with increasing Cu addition was explored. Using average substrate utilisation data at 24 h, the similarity in overall C-use profiles between soil samples was determined using Euclidean distances. Ordination by non-metric multi-dimensional scaling (nMDS) was used to display distances in C-substrate use profiles between soil samples. Cluster analysis (group average method) with SimProf testing (a = 0.05) was used to test for significance of groupings (Clarke 1993). Similarity Percentages analysis (SIMPER; Clarke 1993) was used to explore the contribution (weighting) of individual C-substrates on the overall differences in C-cycling profiles between significantly dissimilar groups. Multivariate data analysis was conducted in the Primer6 software package (PrimerE Ltd., UK) using routines described in Clarke and Warwick (2001).

Total and group-specific bacterial community structures For each sample, community-DNA was extracted from 0.7 g of soil using the MoBio UltraClean soil DNA extraction kit. Mechanical disruption (beadbeating; FastPrep 101) was included to aid in DNA recovery from soil microorganisms. DNA extracts were eluted into TE and stocks stored at 80 1C. Working solutions of 1/10 dilution of stock DNA in sterile, DNA-free water (Sigma) were used for PCR. These solutions were stored at 20 1C. Bacterial community structures were compared using denaturing gradient gel electrophoresis (DGGE) fingerprinting (Muyzer et al. 1993). PCR was used to selectively amplify members of the bacterial community at the domain level (total bacteria). Results later revealed that significant shifts had occurred in the Acidobacteria and Bacillus communities. As such, the diversity (responses to Cu) within these groups, along with the metaltolerant Sphingomonas and ubiquitous Pseudomonas groups were

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also determined. PCR for the total bacterial community used bacterial-specific primers F968-GC and R1401 with touch-down PCR thermocycling conditions described in Duineveld et al. (1998) using chemistry and conditions as described by Wakelin et al. (2007). For all amplification reactions, a final extension step for 20 min at 72 1C was used to remove potential double-bands in DGGE (Janse et al. 2004). Bacillus-selective community profiling was based on the nested PCR method of Garbeva et al. (2003). Primers BACf and R1378 were used in first-round PCR at 0.2 mM each. The reaction mixture also contained 1U of HotStarTaq polymerase (Qiagen), 2.5 mL of 10  buffer, 10 mM of each dNTP, 2 mL of 1/10 diluted DNA and water to a final volume of 25 mL. First round PCR used a touchdown thermocycling profile following the hot-start polymerase activation. Denaturation was conducted at 95 1C for 1 min and extension at 68 1C for 2 min. Annealing was initially at 63 1C (1 min) and reduced by 2 1C per cycle until reaching 55 1C. A further 21 cycles with a 55 1C annealing temperature completed the PCR. Second-round PCR used primers F968-GC and R1378, 2 mL of 1:100 diluted first-round PCR product, and other chemistry as before. PCR amplification consisted of 30 cycles of 92 1C for 30 s, 55 1C for 1 min, and 68 1C for 45 s. Acidobacteria-selective PCR used a nested approach similar to that for Bacillus. First round, Acidobacteria-selective PCR was performed using primers 30F and 1492R (Barnes et al. 1999) and chemistry as described above. PCR amplification was conducted for 30 cycles using 94 1C for 30 s, 42 1C for 30 s, and extension at 72 1C for 1 min. The Acidobacteria-specific PCR product was diluted 1:1000 and a short internal fragment was re-amplified using universal bacterial primers F968-GC and R1378 and conditions described for 2nd-round Bacillus-selective PCR. Sphingomonas-selective PCR followed the method described by Leys et al. (2004). PCR primers Sphingo108f and Sphingo420r-GC were used at 0.5 mM each and chemistry was as described above. After hot-start activation, thermocycling consisted 35 cycles of denaturation at 95 1C for 40 s, annealing at 62 1C for 30 s, and extension at 72 1C for 30 s. Pseudomonas-selective PCR was based on the nested PCR method of Costa et al. (2006). The first round PCR was conducted as described above, using primers F311ps and R1459PS (Milling et al. 2004) at 0.2 mM each. Denaturation was at 94 1C for 1 min, annealing at 63 1C for 1 min and extension at 72 1C for 1 min. After 30 cycles a final 10 min extension step was used. Second round PCR used 0.2 mm each of primers F984-GC and R1378, 4 mL of 1:1000 diluted first-round PCR product, and other chemistry as before. PCR conditions were the same as in the first round PCR, except annealing was at 53 1C. DGGE profiling of the amplified 16S rRNA genes was performed in an Ingeny PhorU system. Profiling of total bacteria used a 7% acrylamide:bis-acrylamide (37.5:1) gel with a formamide:urea denaturing range of 45–55%. For profiling of the soil Bacillus and Acidobacteria communities, 6% gels with 45–65% denaturing range were used, and for Sphingomonas a 6% polyacrylamide gel with a 40–75% denaturing range. Pseudomonas DGGE was accomplished using a double gradient consisting of a formamide:urea denaturing range of 45–65% and 6–9% acrylamide range. In all instances, separation occurred at 60 1C with 110 V for 17 h. DGGE gels were stained in SYBR gold (1  in TAE buffer; Molecular Probes) for 30 min and visualised 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 4th-root transformed and a resemblance matrix generated using the Bray–Curtis algorithm. nMDS ordination was used to analyse

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similarity in soil bacterial communities with increasing Cu (Clarke 1993). Multivariate data analysis was conducted in Primer6 (Clarke and Warwick 2001). The inverse Simpsons index (1 l), a traditional univariate measure of diversity (Simpson 1949), was calculated on non-transformed DGGE band data (Primer6). In the context of this experimental system, the Simpsons index provides a measure of the likelihood of any two randomly selected PCR fragments migrating to the same position on the DGGE gel. Each DGGE band position was inferred to represent a distinct molecular operational taxonomic unit (MOTU), as described in the discussion. The relationship between phylogenetic and functional (BioLog) diversity and soil Cu rates was calculated using segmental linear regression, and the fit of the data compared with single linear regression alone. The intercepts of segmental regressions were used to determine break-point effects of increasing Cu on diversity (GraphPad Prism version 5; GraphPad Software, San Diego). Targeted bacterial identification 16S rRNA gene clone libraries were constructed from soils significantly differing in bacterial communities as a result of Cu addition. The first library was made from DNA extracted from soil that had received no addition of Cu, and the second from the sample receiving 1003 mg Cu kg 1 soil (Cu rates 0 and 9, respectively). The DNA from the two field samples replicates of the above treatments was pooled into a single representative sample and PCR conducted as before using primers R1401 and F968 (no GC clamp). The partial-length 16S rRNA gene PCR amplicons were cloned into the pGEM-T vector system (Promega) and were capillary sequenced from the M13 site (Australian Genome Research Facility). Following removal of flanking vector sequence regions, the taxonomic affiliation of the sequences were determined using a ‘Na¨ıve Bayeian rRNA Classifier’ tool (Wang et al. 2007) within the Ribosomal Database Project V2 (http://rdp. cme.msu.edu/); the assignation of sequences to taxonomy was conducted at 90% confidence threshold; only sequences confidently assigned to taxonomical groups were used for analysis. The sequence libraries generated from the two soil copper rates (104 sequences from Cu 0 and 114 from Cu 9) were compared (library compare tool) and differences in taxa between determined.

Results

The metabolic potential of the microbial community, in terms of average BioLog C-substrate utilisation over 24 h, followed classical dose-response inhibition to both total and extractable soil Cu levels (Fig. 2A). Non-linear dose-response fit of the AWCD data distribution to soil Cu was high, with R2 values of 0.84 and 0.86 for soil CuTot and CuExt data, respectively. At the highest levels of soil Cu ( 41 g CuTot kg 1 soil), soil microbial activity was minimal and was reduced to approximately 3% of the activity observed in unexposed soils. The EC50 inhibition of AWCD occurred at CuTot levels three orders of magnitude greater than CaCl2 extractable levels (Table 2); for soil CuTot, the EC50 was 226 mg kg 1 Cu, whereas for CuExt the EC50 level was 0.21 mg kg 1 Cu.

Bacterial community structure Total bacterial community 16S rRNA gene PCR-DGGE Marked changes in the structural composition of the total (dominant) bacterial community with respect to Cu rates were observed in the soils sampled 5 1/2 years after application (Fig. 1B). Increased dissimilarity between soil bacterial communities was closely tied to increasing soil Cu levels (Fig. 1B). Three significant groupings of bacterial communities were determined. These were associated with Cu rates 0–7, rates 8 and 9, and a final community type associated with Cu rates 10 and 11.

Clone library analysis Clone libraries of 16S rRNA genes were generated from soils identified as having significant shifts in community structure following addition of Cu (Cu rates 0 and 9; Fig. 1B). Sequence analysis of each library identified a shift in soil bacterial composition across taxonomic ranks (Po0.001; Fig. 3). In soil unexposed to Cu, the community was dominated by Acidobacteria (41.1% total Cu 0 sequences), principally the Gp4 genus (Fig. 3). Sequences representative of Acidobacteria were in low abundance (5.3%) within the DNA sequence library generated from the Cu affected (Cu 9) soil. In the Cu 9 soil, Firmicute bacteria, notably the Bacillaceae family, dominated the community (39.5% of total bacteria; Fig. 3). These bacteria were present in relatively low levels in the non-exposed soil (11.2%). Actinobacteria were also more abundant in Cu-exposed soil (35.1% versus 23.4% for unexposed soil) (P= 0.056). Total Proteobacteria, which include the Sphingomonas and Pseudomonas genera, were in relatively low abundance in both unexposed and exposed soils (11.2% and 7.9%, respectively).

Community-level carbon use Increased soil Cu altered the profiles of C cycling in the soil ecosystem (Fig. 1A). Three significantly different (at a = 0.05) groupings of functional profiles were determined, and these corresponded to Cu application rates 0–4, 5–10 and Cu rate 11. The shift in functional profiles between the two larger groups (i.e. Cu 0–4 and Cu 5–10) occurred around the same toxicity point as the EC50 reduction in total catabolic activity (AWCD). The differences in utilisation patterns between these groups were associated with a reduction in catabolism of L-asparagine, b-methyl-D-glucoside and L-arginine with increasing Cu. Similarly, the shift in catabolic profile between the groups ‘Cu rate 5–10’ and ‘Cu rate 11’ was most strongly associated with reductions in b-methyl-D-glucoside, N-Acetyl-D-glucosamine, and a-D-lactose catabolism at the highest rate of Cu addition. The functional profile for sample Cu 3 was found to be vastly outlying from other samples when visualised using nMDS and was excluded from functional analysis.

Group-specific 16S rRNA gene PCR-DGGE Across all phylogenetic groups, significant shifts in community composition were observed between soil samples (Fig. 1C–F) and groupings of statistically-similar communities, associated with increasing soil Cu rates, were identified. The community profiles of Pseudomonas bacteria (Fig. 1E) showed the least amount of gradual change with Cu addition, where the only significant shift in species composition occurred at Cu rates 10 and 11, i.e. over 2 g CuTot kg 1 soil (Fig. 1E). For all other groups, significant shifts in community composition occurred at much lower copper application rates; Cu rate 4 (132 mg CuTot kg 1 soil) for Acidobacteria and Sphingomonas, and Cu rate 6 for Bacillus (297 mg CuTot kg 1 soil). However, the overall trend across all bacterial communities (total and group specific) was for an increasing level of structural change with increasing soil Cu (e.g. Bacillus community).

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Fig. 1. A–F: nMDS plots showing dissimilarity in profiles of (A) soil catabolic functioning (BioLog EcoPlates), (B) total bacterial community structure, (C) Bacillus community structure, (D) Acidobacteria community structure, (E) Pseudomonas community structure and (F) Sphingomonas community structure. Increasing distance between sample points reflects increasing dissimilarity in overall functional profile or community structure. Samples enclosed by ovals are significantly similar at a = 0.05. The low stress values (all o 0.1) indicate accurate two-dimensional scaling (representation) of the ecological distances within the multivariate data sets.

Bacterial community diversity With increasing levels of soil Cu, biodiversity within the total bacterial community and the Bacillus and Acidobacteria subcommunities increased with Cu to a peak level and then sharply declined (Fig. 2B, D and E). For the Sphingomonas and Pseudomonas communities, biodiversity was relatively stable with increasing Cu application rates until a critical soil Cu threshold (break-point concentration) was reached and diversity decreased (Fig. 2C and F). In all cases, the fit of the segmental linear regression,

indicating a unimodal response, was significantly stronger (P o0.05) than if the data was fitted using single linear regression alone (Table 2) Soil Cu levels at which communities were negatively impacted (decreasing diversity) varied widely (Fig. 2B–F; Table 2). The most susceptible group of bacteria were the Acidobacteria, which showed a decline in diversity at 238.8 mg kg 1 CuTot, or 0.131 mg kg 1 CuExt (Table 2; Fig. 2E). The Bacillus and Sphingomonas were the most tolerant bacterial communities, and diversity was

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Fig. 2. A–F: (A) Dose-response curve (R2 = 0.86) fitting the relationship between soil CuExt (mg kg 1 soil) and 24 h catabolic activity (BioLog average well colour development; AWCD). CuExt concentrations reducing the microbial activity by 50% (EC50) are shown as vertical dotted line with 95% confidence intervals in grey shading. (B–F) Relationship between soil CuExt (all as mg kg 1 soil) and the biodiversity (Simpsons index; 1 l) of the total (numerically dominant) soil bacterial community and bacterial groups Bacillus, Acidobacteria, Pseudomonas and Sphingomonas. Diversity based on 16S rRNA gene PCR-DGGE banding profiles. Segmental linear regression was used to determine breakpoints between soil CuExt and biodiversity. Vertical dotted lines intersect the break-point of microbial diversity with respect to Log of soil CuExt.

Table 2 Break points of soil Cu levels (mg kg

BioLog Total bacteria Acidobacteria Bacillus Pseudomonas Sphingomonas

1

soil) leading to declined biodiversity of the bacterial community and groups therein. CuTot mg kg-1 soil

CuExt mg kg-1 soil

Segmental versus single regressiona

226 560 234 2234 1291 2432

0.21 0.75 0.13 10.39 4.61 16.46

n.a. P = 0.0001 P = 0.0035 P = 0.0134 P = 0.0055 P = 0.0055

n.a. =not applicable. a Compares the fit of the biodiversity to soil Cu levels. Where Po 0.05, segmental linear regression (unimodal response) fitted the data significantly better than single linear regression alone.

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Fig. 3. Dominant taxa at a phyla level in the Cu 0 and Cu 9 16S rRNA gene clone libraries. Excluded from the charts are the TM7, Gemmatimonadetes, and Chlamydiae phyla that represented o2% of the overall number of sequences each. Comparison of the total sequences using the Library Compare tool (Ribosomal Database Project) showed that the two clone libraries were highly different in bacterial taxa (P o 0.001). Key differences were in relative abundances of Acidobacteria (Po 0.05), Firmicutes (P o0.05) and Actinobacteria (P =0.056) between the two libraries.

not affected until CuExt was in excess of 10 mg kg 1 soil; i.e. approximately two orders of magnitude greater than the Acidobacteria. The breakpoint for biodiversity within the total soil bacterial community was 560 mg CuTot kg 1 soil or 0.754 mg CuExt kg 1 soil (Table 2). Although these data provide novel insights into the ecology of these taxa, care must be taken not to interpret them from an ecological context (see discussion below).

Discussion A single application of Cu to field soil had extremely pronounced, long-term effects on soil microbial processes, bacterial community composition and the diversity of specific bacterial groups. A shot-gun cloning and sequencing approach showed that shifts in the composition of the total bacterial community occurred at a very high taxonomic level, consisting of a phyla-level shift in dominant abundance from an Acidobacteria-rich community in the un-exposed soil, to a Firmicute (Bacillus)-rich community in Cu-treated soils. Group-specific PCR-DGGE was then used to explore structural changes in the Bacillus and Acidobacteria communities, as these were shown to significantly differ in abundance with increasing soil Cu (Table 2). In addition, community-level responses of Pseudomonas, commonly encountered in culture-based tests for metal contamination or specifically cultured for such studies (e.g. Ellis et al. 2001), and Sphingomonas, a potentially Cu-tolerant group (V´ılchez et al. 2007), were also characterised. Acidobacteria are a widely-distributed, phylogenetically-broad group of soil bacteria. They were virtually unknown prior to the application of rRNA-based analysis techniques, but have now been detected during analysis of community DNA extracted from environmental samples including soils (Ludwig et al. 1997; Hugenholtz et al. 1998; Barnes et al. 1999; Lee et al. 2008). The dominance of Acidobacteria in terms of abundance (rDNA) and activity (rRNA abundance) suggests that they have an important biogeochemical role in the functioning of soil ecosystems (Lee et al. 2008). As such, the strong influence of Cu on these soil bacteria represents an important impact at both a phylogenetic and functional level. The decline in relative numbers of Acidobacteria DNA sequences with Cu was mirrored by a high

sensitivity of diversity (richness) within the Acidobacteria community. The breakpoint at which soil Cu reduced diversity was considerably lower than the levels observed within the total bacterial community and within other bacterial groups tested here. Given that Acidobacteria are both highly Cu-sensitive and represent a high relative proportion of the total bacterial taxa (in uncontaminated soils), it is possible that the behaviour of the total bacterial community is a reflection of the ratio of Acidobacteria to Cu-resistant groups. As such, the ecological resistance of the total bacterial community to Cu stress would be strongly moderated by the abundance of Acidobacteria. The structure of the soil bacterial community shifted towards dominance in Bacillus species with addition of Cu. However, as the data generated here were relative (i.e. percentage phyla in each rRNA library) we did not conclusively show that Bacillus became more absolutely abundant. Bacillus species are capable of forming highly-resistant endospores, and are known to be highly resistant to environmental stress including those imposed by metals such as Pb and Cu (Roane and Kellogg 1996; Kunito et al. 1997; Zhang et al. 2007), and have also been implicated in the biogeochemical cycling of metals such as Au (Reith and Rogers 2008). Despite this, the consensus of previous work is towards a reduction in Bacillus and other Gram-positive bacteria with Cu contamination (e.g. Sandaa et al. 1999, 2001). The discrepancy between these studies and our findings may be due to methodological approaches, differences in soils or experimental systems, or short versus longterm contamination. In particular, previous work reliant on culture-based studies will be heavily biased towards easily cultivable genera such as Pseudomonas and Bacillus, and the results of such studies cannot be validly compared to this work (Zhang et al. 2007). Regardless, this study is one of the first to show phylotypic shifts within the Bacillus community and the maintenance of a high level of phylotype richness with respect to soil Cu levels. These results highlight the potential importance of this group of bacteria in maintaining ecosystem functioning in disturbed systems. The responses of Pseudomonas and Sphingomonas bacteria to Cu were also explored. These bacterial groups represent ubiquitous and well-studied g-Proteobacteria (Pseudomonas) and stress-tolerant, but low-abundance a-Proteobacteria (Sphingomonas). Sphingomonas, in particular, has been shown to be resistant

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to Cu addition to soil (e.g. Sandaa et al. 1999) and may have a wide physiological tolerance to Cu (V´ılchez et al. 2007) and stress factors in general (White et al. 1996). Our results support these findings – Sphingomonas was the bacterial group most resistant to Cu (in terms of species biodiversity), tolerating orders of magnitude more added CuExt before biodiversity loss relative to susceptible phyla such as Acidobacteria. Pseudomonas was neither particularly susceptible nor particularly resistant to Cu relative to other groups of bacteria tested here. Importantly, however, the community diversity declined sharply and rapidly when the Cu break-point level was reached. Previous studies have alternately shown Pseudomonas populations to succeed in the presence of Cu (Abaye et al. 2005), or to be susceptible to Cu (Ellis et al. 2001). It is possible that such inconsistencies in Pseudomonas (and potentially other bacterial groups) are a result of testing across either side of this break-point in different studies. The ability of Pseudomonas to tolerate Cu has been shown to be conferred by specific Cu resistance genes such as copA (Cooksey 1994). The diversity of these genes in environmental samples has provided insights into the ecology and adaptation of bacteria in contaminated soils (Lejon et al. 2007). We have also conducted copA PCR-PAGE fingerprinting across the range of Cu contaminated soils used in this study (unpublished data). Shifts in the copA profiles with Cu were evident, and these shifts followed shifts in bacterial community structures (i.e. nMDS profiles). Genetic determinants of Cu resistance, such as the copA gene, may have been important to support community stability of Pseudomonas and other similar g-Proteobacterial groups. However, given the low representation of g-Proteobacteria in the clone libraries ( o3% total sequences), the significance of this within a wider ecological context is unknown. For a number of reasons, the soil Cu values from which the diversity breakpoints were derived should not be viewed as indicative values for any sort of ecotoxicological use. Importantly, we have not estimated the degree of variation in these data points (this was not possible given our experimental system and modelling approach), nor can we say that that the Cu break point for one group of taxa is significantly different to that of another. In addition, the slope of the initial regression line, and subsequent intersect/breakpoint, is susceptible to leverage from the first Cu values. In this work, the inclusion of these data points, to which we ascribed a small nominal value, was preferable as it correctly reflected the behaviour of the initial regression line in accordance to what was biologically observed – i.e. the (generally) low biodiversity at the ‘zero’ Cu treatment level. Microbial function, assessed using BioLog profiling, followed a strong dose-response trend with increases in concentrations of added Cu. Significantly, the EC50 for loss of catabolic function was at around 225 mg CuTot kg 1 soil (Table 2; Fig. 2A), coinciding with reduction in diversity of the dominant bacterial, Acidobacteria, at 238 mg CuTot kg 1 (Table 2; Fig. 2E). Again, this link supports the proposed important role of Acidobacteria in general soil C-mineralisation processes. The BioLog method (generally using GN-type BioLog plates) has previously been shown to reliably detect differences in community-level microbial functions in metal contaminated soils  (Knight et al. 1997; Ellis et al. 2001; Niklinska et al. 2006). In the current study, the tight response curve between function and contamination was particularly striking given the duration (5 1/2 years) between the addition of Cu to the soil and the sampling and the analysis conducted here. Furthermore, previous long-term metal exposure studies have shown that microbial activity may fully recover in soils despite permanent changes in the microbial community structure (Turpeinen et al. 2004). In this study, changes in microbial community structure and loss of catabolic function both shifted after long-term exposure to

Cu. If we assume that the most abundant bacterial groups are responsible for a large component of general biogeochemical cycling, such as mineralisation of relatively simple C compounds found in BioLog plates, we can conclude that the shift in bacterial groups (but not diversity per se) is linked with a change in longterm microbial function. This may be driven by the ecological fitness of various bacterial groups in soil habitats. In the absence of added Cu, Acidobacteria were the dominant phylum in the Spalding field soil habitat – we can presume that their fitness extends to the ability to mineralise C in this soil environment. Addition of Cu induced a community shift towards increasing dominance by Bacillus, a group perhaps less well adapted to the soil habitat per se (or they would be as abundant as Acidobacteria) and also less suited to mineralisation of C within the environmental parameters of the Spalding soil. The use of BioLog plates to assess microbial processes in the presence of metals, and particularly Cu, has previously come under criticism due to interference of metals with the dehydrogenase assay method (Chander and Brookes 1991). To address this, researchers have alternatively used dilutions of soil extracts to ensure Cu concentrations were below those found to cause dehydrogenase inhibition (e.g. Knight et al. 1997), used cationexchange resin to remove free metal ions from solution (Kelly and Tate 1998; Kelly et al. 1999), have compensated by spiking standard amounts of metals into wells, or have ignored this issue. In this work, the total dilution of soil before addition to BioLog plates was approximately 1:100, which would concomitantly reduce CuExt levels by 2 orders of magnitude. We also found that Cu effects were not uniform across the range of substrates within the BioLog plates, indicating that the effect was not due to chemical action. In subsequent experiments, we have also measured CO2 evolution from basal and substrate induced (medic stubble) treatments across these Cu dose-response ranges (data not shown) and have shown long-term impacts on microbial function in these Cu-affected soils. The consensus of this data leads us to conclude that the responses we have identified in the BioLog assays represent real impacts on soil microbial functions. Short-term experiments do not accurately reflect the longterm responses of Cu contamination in the environment (Oorts et al. 2006). After 6 months, Oorts et al. (2006) found no appreciable reduction of Cu toxicity in soil; however, fresh spiking of Cu to soil directly affected microbial C and N cycling processes (Oorts et al. 2006). Such effects are likely to be a combination of both chemical and biological factors. Upon the addition of Cu to soil, high salt concentrations in soil solution can exacerbate metal toxicity (Stevens et al. 2003) and rapid chemical ageing reactions initially decrease Cu availability. These rapid reactions are followed by much slower, long-term aging processes that reduce availability over time (Ma et al. 2006). In addition, microbial communities can adapt or show tolerance to soil Cu over time  (Fait et al. 2006; Niklinska et al. 2006; van Beelen et al. 2004). Studies exploring community-level microbial responses in long-term field sites have largely been restricted to the addition of metals within biosolid waste (Abaye et al. 2005; Macdonald et al. 2008) and/or as a component of a mixed-metal contamination such as occurs at disused industrial facilities or mine sites (Oliveira and Pampulha 2006). Furthermore, in unmanaged systems, plant communities have been shown to shift with Cu addition (Strandberg et al. 2006). As the above-ground botanical composition is an important driver of below-ground microbial community composition (e.g. Garbeva et al. 2006), this represents a major interactive factor which requires uncoupling from metalspecific effects. At the Spalding field trial site, we were able to specifically show that Cu addition resulted in long-term, community-level shifts. As the trial plots experienced a constant selective pressure with regards to botanical composition (wheat), the

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results are reflective of primary-level metal effects. Furthermore, we were able to establish that the microbiological responses were due to the Cu per se and not an artefact of a shift in soil pH (Cu was added to the field soil as CuSO4). At the time of sampling, a roughly linear drop in pH across the Cu gradient was still evident. Although this was only 0.5 units, pH has been shown to be a key driver of soil bacterial community structure and function (e.g. Fierer and Jackson 2006; Wakelin et al. 2008). Field plots in which a Zn-based dose response had been setup in parallel to the Cu experiment (Broos et al. 2007) were also sampled and analysis conducted. In these samples, no responses to Zn were observed across nearly all of the application range (Suppl. Fig. 1). As the Cu-type microbial responses were not observed in the Zn data set, we conclude that the microbial responses were due to the Cu itself and not the minor shift in soil pH. The use of statistical measures, and particularly diversity indices, following rRNA PCR-fingerprinting of microbial communities has been the subject of criticism. Principally, concerns have been raised regarding (1) the degree to which DNA extracted from soil reflects the actual community (extraction efficiency across phyla), (2) how well rRNA PCR products reflect the community they target (primer bias/amplification artefacts), and (3) how accurately post-PCR fingerprinting methods reflect genotypic variation within the PCR product. Limitations associated with the first two points are both appreciated and accepted; despite this, analysis of PCR-amplified rRNA fragments remains the most important and widely used method in microbial ecology studies. It is somewhat peculiar, therefore, that the post-PCR fingerprinting approach receives a great deal of attention with regards to statistical interpretation of the data. A key aim of this work was to explore the nature of the bacterial biodiversity response to Cu and to test the IDH. In most bacterial groups tested, diversity did not linearly nor even monotonically decline with Cu addition, but rather exhibited a unimodal response – i.e. increased with low to medium levels of added Cu and then declined (often sharply) after a critical level. Such unimodal responses of biodiversity to stress disturbance have been widely reported in macroecological literature (e.g. Connell 1978; Sousa 1979) but reports are much rarer in microbial ecosystems. The Spalding field trial was an ideal site to test the IDH on microbial communities given the wide dose range of Cu present in the soils, and also due to a lack of external factors confounding the Cu-stress (discussed previously). As such, we can fully support the notion of Giller et al. (1998) questioning the assumptions of direct negative relationships between soil bacterial communities and metal contamination.

Acknowledgements We thank the following people from CSIRO Adelaide for their assistance in this project: Adrienne Gregg (molecular microbiology), Marcus Hicks (BioLog), Gill Cozens and Michelle Smart (soils), and Cathy Fiebiger (chemistry). Kris Broos provided advice on handling of soil chemistry data for (Cu) toxicology determination, and also general aspects of soil microbial ecology in contaminated soil ecosystems. We also thank the Cootes family, Spalding, for hosting the project field site. G.X. Chu visited CSIRO Land and Water (Adelaide) as part of a ‘State-Sponsored Scholarship for Advanced Scholars Program’ awarded by the China Scholarship Council (CSC). S.A. Wakelin was awarded a Julius Career Award (CSIRO-OCE) which was instrumental in supporting this work. Drs Kris Broos, Jelle Mertens and 2 anonymous referees provided critical review of this manuscript. We also thank the staff at GraphPad Software Inc and PrimerE Ltd. for provided excellent support for the analysis of the data.

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Appendix A. Supplementary material Supplementary data associated with this article can be found in the online version at doi:10.1016/j.pedobi.2009.09.002.

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