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Archaeal ammonia oxidisers are abundant in acidic, coarse-textured Australian soils.


The microbially mediated conversion of ammonium-nitrogen (N[H.sup.4.sup.+]-N) to nitrite-N (N[O.sub.2.sup.-]-N) is the first step in the process of nitrification, i.e. the oxidation of N[H.sup.4.sup.+] to nitrate (N[O.sub.3]) (Focht and Verstraete 1977). While NO3 production is important for plant N uptake, N[O.sub.3.sup.-]-N is also susceptible to leaching into water bodies or loss to the atmosphere through denitrification. Together, nitrification and denitrification are viewed as important mechanisms affecting N loss in soils thereby lowering the N-use efficiency. In agricultural systems, it is desirable to control the conversion of N fertiliser applied in stable, reduced forms (N[H.sup.4.sup.+], urea) to more mobilc, oxidised forms (N[O.sub.3]), as this allows for longer access to the N by plant roots (Chen et al. 2008a) and increases the N-use efficiency of fertilisers. The production of nitrous oxide, a powerful greenhouse gas and ozone-depleting agent, during both nitrification and denitrification is yet another reason for controlling the flow of N through the nitrification pathway (Dalai et al. 2003).

Historically, it was assumed that the process of ammonia oxidation, the rate-limiting step of nitrification, was restricted to a small range of autotrophic bacteria belonging [beta] and [gamma] subclasses of the Proteobacteria (Head et al. 1993; Bothe et al. 2000). Collectively known as the ammonia-oxidising bacteria (AOB), these microorganisms have been extensively used to study the biochemistry and physiology of nitrification (Bruns et al. 1999; Avrahami et al. 2002; Carnol et al. 2002; Bfickman et al. 2004; Bowatte et al. 2006; Chu et al. 2007). The recent discovery of ammonia-oxidising capacity within the Crenarchaeota lineage of the archaea (Treusch et al. 2005; Vcnter et al. 2004) challenges the assumption that nitrification in soil is chiefly the domain ofAOB. Due to their relatively recent discovery, the ammonia-oxidising archaea (AOA) are poorly understood compared with the AOB. It has been suggested that the AOA are favoured over AOB at lower soil pH (Nicol et al. 2008) and low N[H.sup.4.sup.+] availability (Martens-Habbena et al. 2009). Those findings suggest that important physiological and biochemical differences exist between AOA and AOB. Therefore, more information is needed about the ubiquity of these functional groups of organisms within soils and their role in terrestrial N cycling.

The [alpha] subunit of ammonia monooxygenase (amoA), the enzyme which catalyses the first step in the ammonia oxidation reaction, has been used by numerous authors as a functional gene marker to detect the bacterial and archaeal ammonia-oxidising populations (for review see Junier et al. 2010). Using this functional gene target, it has been demonstrated that AOA-type amoA copies often exceed those from bacteria in both soils and marine waters (Francis et al. 2005; Leininger et al. 2006; Chen et al. 2008b; Bowatte et at 2009). It has also been shown that these genes are being transcribed in natural systems, suggesting that the AOA are active ammonia oxidisers and contribute towards nitrification (Leininger et al. 2006; Nicol et al. 2008).

It has been suggested that AOA may play an important/ dominant role in soils with low pH and/or in low-N environments (Erguder et al. 2009). Western Australia's south-west agricultural region is dominated by sandy soils with low pH (Dolling et al. 1990). These soils have low water-holding capacity and are subject to leaching of nutrients (Anderson et al. 1998; Milroy et al. 2008). These conditions lead to, among other things, relatively low N-use efficiencies (Fillery and McInnes 1992). The soil conditions that dominate agricultural soils in Western Australia fit the description of those favouring AOA.

The aim of this study was to assess the abundance of AOA relative to AOB in acidic soils in the arable zone of Western Australia and to investigate the relationship between the microbial community structure, soil physicochemical properties, soil N content, and potential nitrification rate (PNR).

Materials and methods sample collection and storage

Soils were collected in summer from the Hanks dairy farm (Hanks site) near Harvey (33[degrees]05/00"S, and 115[degrees]53'35"E) and the Alcoa Farmlands property (Stage 3, Blakes, and Driveway sites) near Waroona (32[degrees]50'40"S, 115[degrees]50'20"E). All paddocks sampled received irrigation.

The Hanks farm represented an 'intensely managed' agricultural system with regular application of lime for pH control (2.5t/ha.year in 2005, 2006, and 2007 and 1.5t/ha. year in 2008 and 2009), and high N input (360kgN/ha.year). Fertiliser-N was applied approximately monthly following grazing events. In contrast, the Stage 3 and Blakcs sites on the Alcoa Farmlands received 65 kg N/ha.year and lower inputs of lime (1 tJha in the last 5 years). The Stage 3 and Blakes sites represented less-intensively managed pastoral systems. The Alcoa Driveway site did not receive fertiliser N or lime and it was not surface-irrigated and represented an 'unmanaged' system.

At each site, soil was collected in three layers (0-2, 2-5, and 5-10 cm) using a 2-cm-diameter, hand-driven auger. It has been shown that microbial activity, including nitrification, is highly stratified in no-till systems, with the majority of activity occurring in the upper layers (Kandeler and Bohm 1996). For this reason, sampling was focussed on the upper 10 cm of the soil profile. At each sample site, soil was sampled at about 10-m intervals along a transect of ~100m. The 10 subsamples from each transect were pooled according to depth. Triplicate transects were sampled in all of the paddock sites; no replicates were collected from the Driveway site. samples were placed in zip-lock bags and transported on ice (~3-5hours) to the laboratory where they were stored at 4[degrees]C for 4 days until further processing. Prior to analysis, samples were well mixed and subsampled to give separate samples for DNA extraction, nutrient analysis, water content determination, and nitrification rate incubations.

DNA extraction

Extraction of DNA was competed 1 week after collection, using a commercial kit (PowerSoil DNA extraction kit; MO BIO Laboratories Inc., Carlsbad, CA, USA). A 0.25-g subsample from each soil sample was processed for DNA extraction according to the manufacturer's instructions, except that mechanical disruption of cells was carried out in a FastPrep beadbeater (FP120; Qbiogene Inc., Carlsbad, CA). Extracted DNA was quantified using the Quant-iT PicoGreen based method (lnvitrogen, Mulgrave, Vic., Australia) with measurement conducted on a Stratagene Mx3000P PCR system (Agilent, Santa Clara, CA). DNA quantifications were referenced to a standard curve created of serially diluted K-phage DNA that had been quantified spectrophotometrically (NanoDrop, Thermo Scientific, Scoresby, Vic., Australia). All DNA extracts were diluted to a working concentration of 1.9 ng DNA/[micro]L. Extracted DNA samples were stored at 80[degrees]C.

Quantification of arena gene copy number

Real-time, quantitative PCR (qPCR) was used to quantify copy numbers of the amoA genes from bacterial and archaeal populations. Archaeal amoA genes were amplified with the Crenamoa23f and Crenamoa616r primers described by Tourna et al. (2008) using a SYBRGreen based detection. amplification reactions were carried out in 25-[micro]L volumes containing 1.5[micro]L of each 10mM primer (lnvitrogen, Australia), 12.5[micro]L QuantiTect SYBRGreen Master Mix (QIAGEN Pty Ltd, Doncaster, Vic., Australia), 0.5 [micro]L bovine serum albumin (Roche, Castle Hill, NSW, Australia), 5 [micro]L DNA template, and 4[micro]L DNAse-free water. The PCR conditions consisted of a hot start incubation of 95[degrees]C for 5min, followed by 40 cycles of 94[degrees]C for 45 s, 53[degrees]C for 1 min, 72[degrees]C for 1 min, followed by a final step of 72[degrees]C for 10min.

Bacterial amoA genes were similarly quantified using SYBRGreen-based qPCR. The PCR used primers amoA-1F and amoA-2R (Stephen et al. 1999) as modified from Rotthauwe et al. (1997), and reaction chemistry was based on the Brilliant SYBRGreen Master Mix (Agilent, Integrated Sciences, Chatswood, NSW). Each 25-[micro]L reaction had 1/[micro]L of each primer (at 10raM each), 12.5 [micro]L Master Mix, 0.5 [micro]L bovine serum albumin (Roche), and 5 [micro]L DNA template water to bring the final volume up to 25 [micro]L. The PCR conditions consisted of a hot start at 95[degrees]C for 10 min followed by 40 cycles of 95[degrees]C for 45 s, 55[degrees]C for 1 min, 72[degrees]C for 45 s, and a final step of 72[degrees]C for 10 min.

All qPCR amplifications were carried out on a Stratagene Mx3000P PCR system (Agilent, CA). Cycle threshold (Ct) values (the threshold value above which all amplification reactions are in the exponential phase) were determined by the software and did not require manual adjustment. The qPCR products from both the archaeal and bacterial qPCRs were run on a 1.5% agarose gel and visualised after staining with ethidium bromide to confirm that amplification resulted in a single band of the expected size.

In order to calculate DNA concentrations from Ct values, standard curves were constructed by quantifying the gene copy numbers in a dilution series created from plasmids (pOEM-T, Promega, Alexandria, NSW) into which had been ligated amoA gene of Nitrosomonas europaea (ATCC 19718) or archaea amoA PCR product from an environmental sample. Cloning and preparation of the AOA amoA standard from the environmental sample has been described previously (Wakelin et al. 2009).

All quantification results are presented as the gene copies per unit mass of total microbial DNA extracted from the soils to account for differences in extraction efficiencies. Several studies have shown that DNA extraction processes, especially those using commercial kits, have varying efficiencies depending on the method used and the soil type (Zhou et al. 1996; Frostegard et al. 1999; Roose-Amsaleg et al. 2001; Carrigg et al. 2007). This means that normalising gene counts by soil mass may introduce artefacts into the data due to differences in DNA extraction efficiencies between samples. Expressing gene counts per ng of DNA extracted from the sample overcomes this problem. This is a relative unit of abundance; it expresses the amount of AOA or AOB amoA genes as a fraction of the overall community (expressed as total mass of DNA extracted). Therefore, differences in the total amount of DNA extracted per g of soil do not impact on the data.

Archaeal community diversity analysis

The diversity of the archaeal ammonia-oxidising community was investigated by denaturing gradient gel electrophoresis (PCR-DGGE) fingerprinting of AOA amoA genes. Amplification of the archaeal amoA genes from the soil DNA extracts was carried out on an Eppendorf Mastercycler PCR cycler using the Crenamoa23f and Crenamoa616r primers (Tourna et aL 2008). Each 25-[micro]L reaction volume contained 1.5[micro]L of each primer (Invitrogen, Australia), 2[micro]L dNTP mixture (2.5 mM each; Invitrogen, Australia), 1.5[micro]L Mg[Cl.sub.2] solution, 0.5[micro]L bovine serum albumin (Roche), 0.3[micro]L 'lmmolase' Taq polymerase (Bioline (Aust) Pty Ltd, Alexandria, NSW), 2.5[micro]L 10x reaction buffer, 5[micro]L DNA template, and 10.2[micro]L DNAse-free water. The PCR cycle conditions were 95[degrees]C for 5 min (hot start) followed by 35 cycles of 94[degrees]C for 45 s, 53[degrees]C for 1 min, 72[degrees]C for 1 min, and a final extension step of 72[degrees]C for 10 min.

A small portion (2 [micro]L) of each PCR product was run on a 1.5% agarose gel to confirm successful amplification of a DNA fragment of the expected length. The remaining PCR products were subjected to DGGE on a PhorU electrophoresis system (Ingeny, Goes, Netherlands) with gels containing a linear formamide/urea gradient of 15 55%. The gel was run for 17 h at 110 V. After electrophoresis, the gel was stained with SYBR Gold nucleic acid stain (lnvitrogen, Australia) for 30 min and then visualised on a Dark Reader (Clare Chemical Research Inc. Dolores, CO, USA). The gels were photographed with an Olympus SLR digital camera. The dominant bands from the DOGE gel were excised, cloned and sequenced (see Supplementary Materials for detailed methods available on journal's website).

Due to low levels of detection, DOGE fingerprinting of AOB amoA was not conducted.

Chemical and physical analysis

The following analyses were undertaken on all soil samples. The concentrations of N[H.sub.4.sup.+] and N[O.sub.3.sup.-]-N +N[O.sub.2.sup.-]-N were analysed after extraction of 20 g of soil in 100 mL of 1 M KC1 solution for 1 h (Keeney and Nelson 1982). Concentrations of N[H.sub.4.sup.+]-N and N[O.sub.3.sup.-]-N in the extracts were determined by continuous flow analysis on a TrAAcs 800 autoanalyser (Bran + Luebbe, Norderstedt, Germany). Water content was determined gravimetrically after drying at 105[degrees]C for 24-48h. Soil pH was measured after suspension in 0.01 M Ca[Cl.sub.2] at a 1:5 soil:solution ratio. Total N and carbon (C), soil texture, and % volatile solids were estimated by mid-infrared spectroscopy (Forrester et al. 2003).

The PNR was determined using the shaken slurry method (Hart et al. 1994). Briefly, 15 g of soil was placed in 100 mL of medium containing 1 mM P[O.sub.4.sup.3+] and 1.5 mM N[H.sub.4.sup.+] and incubated at 26[degrees]C in a shaker incubator. Slurry samples were collected after 2, 4, 22, 24, and 50 h. The additional sampling after 50 h was used, as nitrification rates in some samples were very low. Slurry samples were filtered and the N[H.sub.4.sup.+]-N and N[O.sub.3.sup.-]-N content of the filtrate determined by continuous flow analysis on a TrAAcs 800 autoanalyser. The PNR was determined by completing a linear regression of N[O.sub.3.sup.-]-N generation over the incubation time as described by Hart et al. (1994). All PNR assays were performed in triplicate, and variability is presented as the standard error of the mean from the three data sets.

Statistical analyses

The DOGE gel image was analysed for band position and intensity with TotalLab software Phoretix (TotalLab Ltd, Newcastle upon Tyne, UK). Background signal was removed using the rolling ball method. Each band position was inferred as a distinct operational taxonomic unit (OTU) and relative band intensity was used as an indication of relative abundance (discussed in Fromin et al. 2002).

The band position and intensity were then taken into the PRIMER 6/PERMANOVA software package (PRIMER-E Ltd, lvybridge, UK; Clarke and Gorley 2006) for multivariate analysis of the amoA communities (genotypes). Band intensity data were fourth-root-transformed to down-weight highly abundant genotypes. A resemblance matrix was constructed using the Bray-Curtis algorithm, and then distances between samples were resolved using non-metric multi-dimensional scaling (nMDS). The effects of site and depth (within sites) on amoA genotypes was formally tested using permutational multivariate analysis of variation (PERMANOVA; Anderson 2001). In the design, site was used as a random factor and depth was fixed. For the test, 999 permutations were used with the residuals calculated under a reduced model (Anderson et al. 2008).

Links between AOB amoA genotypes and the soil physicochemical properties were made using the BIO-ENV test (biota and/or environment matching; Clarke 1993; Clarke and Ainsworth 1993). This test selects for environmental variables that maximise the rank (Spearman) correlation to a fixed resemblance matrix (amoA genotypes). Permutation of variables (499 permutations) was used to generate a null distribution from which significance testing of the rank correlations can be made. The fixed, amoA genotype distance matrix was generated using the Bray-Curtis method. For soil physicochemical properties, the data set was first normalised and a resemblance matrix constructed using Euclidean distances.


Soil physical and chemical properties and potential nitrification rates All soils sampled in this study were acidic with pH(Ca[Cl.sub.2]) ranging from a maximum of 5.8 in the surface layers of Hanks to a minimum of 4.1 in the 5-10 cm layer at the Blakes site. A trend of decreasing pH with soil depth was evident at all sites (Table 1).

Soils collected from below 2 cm at Hanks and Stage 3 were loams, whereas Blakes and Driveway soils were sandy loams. The top 2 cm of soil at all sites contained large amounts of organic C, with the pasture thatch particularly evident in the Stage 3 surface layer (75% dry weight volatile solids). Given the very high organic content of Stage 3 soil, it is likely that the data from the mid-infrared analysis were less accurate for Stage 3 than for the other soil samples. Total organic C and total N contents decreased with depth at all sites.

Concentrations of KCl-extractable N[H.sub.4.sup.+]-N varied between sites but were generally highest in the top 2 cm (Table 1). Over a soil depth of 5 cm, Hanks contained 3.3 kgN/ha, Stage 3 had 5.9kgN/ha, Driveway 9.1kgN/ha, and Blakes 6.8kgN/ha. Samples from the Hanks site contained appreciable N[O.sub.3.sup.-]-N at all depths, while the contents of N[O.sub.3.sup.-]-N at all depths at the other three sites were considerably lower than at Hanks. (Note: N[O.sub.3.sup.-]-N +N[O.sub.2]-N were measured in combination, but since N[O.sub.2.sup.-]-N is known to be maintained at very low levels in soils by rapid conversion to N[O.sub.3.sup.-]-N by nitrite oxidisers, this term is condensed to N[O.sub.3.sup.-]-N throughout this paper.)

The PNR varied with both site and depth (Fig. la), being highest in the upper layers of the most highly managed site, Hanks, at 3.5 mg N[O.sub.3.sup.-]-N formed/kg dry soil.h. The PNR at this site dropped sharply with depth (Fig. la). PNR was also higher in surface layers than in deeper layers of the Blakes and Driveway sites, although significantly lower than recorded at the Hanks site. Nitrate formation was not detected during PNR tests on samples from any depth at the Stage 3 site.

Comparison of Fig. la and Fig. lb clearly shows that that the changes in PNR with depth were not correlated to changes in either the AOA or AOB amoA gene counts.

Relative abundance of AOA and AOB

Successful amplification of archaeal amoA genes (-600 bp) with both traditional PCR and qPCR confirmed the presence of AOA in all samples. Figure 1b shows that copy numbers of archaeal amoA genes varied between a minimum of 101.6 copies/ng DNA extracted in the surface layers of the Stage 3 site and a maximum of 105x copies/ng DNA extracted in the 5-10 cm layer of the Blakes site. A trend of increasing number of AOA amoA gene copies with depth was evident at all sites.

Amplification of AOB amoA genes proved more difficult. Quantification of bacterial amoA genes by qPCR was not possible in samples from three of the four sites as they were below detection limits. AOB amoA genes were detectable from the Hanks site at abundances between 102 and 102.8 copies/ng DNA (Fig. 1 b), giving an AOA/AOB ratio of 1.3, 1.1, and 4.6 in the 0-2, 2 5, and 5-10cm layers, respectively, for the Hanks site.

A strong and significant correlation existed between soil pH and abundance of archaeal amoA gene copies at each site (Spearman r=0.8, P-0.003). This trend is evident in Fig. 2; as soil pH decreased with soil depth, AOA amoA abundance increased. However, Fig. 2 also shows that site was likely to play a role in determining AOA abundance, with two slightly different slopes occurring at the two different properties. The Blakes, Stage 3, and Driveway sample sites were all on different areas of the same property, whereas the Hanks site was on a different property ~50 km away.


The inability to detect AOB in soils from nine of the 12 samples precluded statistical analysis. However, it should bc noted that AOB were only detected at the site with the highest pH readings, i.c. where the soil was subject to regular lime treatment.

DGGE analysis of AOA

The DGGE analysis of the AOA communities revealed high diversity in these soils, as demonstrated by the large number of bands produced in each sample and the variation between banding patterns with both depth and site (Fig. 3a). The results of sequencing and phylogenetic analysis of the dominant DGGE bands are presented in Supplementary Material. Given the low level of detection of AOB amoA, DGGE fingerprinting between soils was not conducted.

Ordination of AOA community structure using nMDS (Fig. 3b, c) showed influences of both site and depth. The Stage 3 samples are quite different to the other soils (in terms of both this analysis and the physico-chemical data), and there is a lot of variation within those samples. Removing the Stage 3 samples from the analysis permitted a better fit (with the stress falling from 0.123 with Stage 3 samples to 0.052 without Stage 3 samples) and revealed a more pronounced depth interaction in the remaining samples (Fig. 3c).

Formal testing of treatment effects (PERMANOVA) revealed that the structure of AOA community (amoA DGGE banding patterns) significantly varied according to site (P=0.001) but not depth (P=0.1). However, following removal of the Stage 3 samples, significant effects of both site (P-0.001) and depth (P 0.021) were found. Regardless, the effects of site were much stronger than those of depth (site [square root]CV = 42, depth [square root]/CV = 18, residual [square root]/CV= 19).


BEST analysis, used to link environmental properties to AOA community structure (excluding Stage 3 soils), found pH(Ca[Cl.sub.2]) to be highly correlated with (9=0.72; P-0.005) amoA genotypes. Adding other variables only slightly increased this correlation, indicating that pH is an important driving factor in AOA community structure.


This is one of the first Australian studies to show the relative importance of AOA and AOB to the nitrification process in agricultural soil collected from commercial farm paddocks under different management. The results show that AOA amoA genes were more abundant than those from AOB, and that AOB were below detection limits at several of the study sites. While PNR were lower at the AOA-dominated sites than the AOB-dominated sites, nitrification activity was still detectable at all sites except Stage 3. This suggests that AOA are driving nitrification at sites where AOB could not be detected.


The presence of AOA in Australian soils was first confirmed by detection of archacal amoA genes, the sequences of which were deposited into GenBank in 2005 (Wakelin and Stephen 2005, GenBank accessions DQ304862-DQ304896). However, other than the submission of the sequences to the database, this work has not been published in the literature and information regarding the ubiquity and ecology of AOA in Australian soils remains sparse. Mertens et al. (2009) published an investigation of the impact of zinc contamination on the ammonia-oxidising community in South Australian soils. While AOA were numerically dominant in these soils, it was AOB not AOA that dominated growth and ammonia-oxidation activity in soils recovering from zinc contamination. More recently, those researchers have found that both AOA and AOB play a role in soils adapting to copper contamination (Mertens et al. 2010). To date, the only other published works on AOA within Australia are an investigation of marine AOA in estuarine sediments in Queensland (Abell et al. 2010) and a study examining the response AOA and AOB to changes in waterfilled pore space (Gleeson et al. 2010).

Ammonium availability is an important determinant of nitrification rate in soils (Shaviv 1988) and, from recent work, plays a key role in determining ammonia-oxidising community structure, with high ammonia loads favouring AOB and low N[H.sub.4.sup.+] availability favouring AOA (Erguder et al. 2009; Diet al. 2010). The N[H.sub.4.sup.+]-N contents in soils used here (range 3.3-9.1 kg N/ha) are higher than reported by Murphy et al. (1998) for soils under pasture at Beverley in the wheatbelt of Western Australia (range 1.7-8.1 kgN/ha for similar depth), indicating that N[H.sub.4.sup.+] availability was not likely limiting nitrification in the soil used here. In contrast, N[O.sub.3] concentrations are relatively low compared with those reported by Anderson et al. (1998) for Western Australian wheatbelt soils, suggesting a low in sire nitrification rate and/ or loss of nitrate due to leaching.

However, the PNR measured in these soils, while variable, are in line with rates given elsewhere in the literature for acidic soils (He et al. 2007; Boyle-Yarwood et al. 2008; Schepers and Raun 2008). The higher contents of N[O.sub.3.sup.-] at Hanks, even though the concentrations of N[H.sub.4.sup.+] were lower on average than at other sites, are in keeping with the higher nitrification rates recorded in the PNR tests for Hanks. It could be argued that the comparatively higher PNR at the Hanks site were associated with the presence of AOB in these samples, while the low rates at the other sites were due to the lack of AOB. However, more data would be required to confirm this when the lack of correlation between PNR and community structure seen in Fig. 1 is considered. Bowatte et al. (2009) noted that the nitrification activity of New Zealand soils varied widely and speculated that differences in the ammonia-oxidising community (bacterial v. archaeal) was a possible reason for this finding. However, the issue of whether AOA or AOB abundances are correlated to PNR in soils remains unclear. In some studies, AOA abundances have been linked to increased nitrification rates (Gubry-Rangin et al. 2010; Wessdn et al. 2010; Yao et al. 2011), while in others, AOB numbers have been positively correlated with nitrification rates (Ying et al. 2010; Wang et al. 201 l). Still others suggest that neither group can be correlated with PNR (Bernhard et al. 2010). This lack of dominance of one group over the other in regard to driving nitrification rates could be due to site-dependent environmental differences which favour different groups at different sites (e.g. pH, N content, moisture content, plant community, etc.) or it could reflect functional redundancy between the two microbial groups.

The pattern of increasing AOA with depth and decreasing PNR with depth, noted in this study, has been reported for New Zealand soils (Bowatte et al. 2009; Diet al. 2010). Other studies have also noted that the higher abundance of archaeal amoA gene copies relative to bacterial amoA genes does not necessarily translate to higher nitrification rates by the archaea (Leininger et al. 2006; Boylc-Yarwood et al. 2008; Diet al. 2009). However, it should be noted that AOA were almost certainly responsible for nitrification in the Blakes and Driveway sites despite the lack of correlation between AOA amoA gene counts and PNR, since AOB were not detected.

Although there are several reports in the literature showing that AOA are abundant in many terrestrial and marine ecosystems, it is still unclear what environmental conditions favour their abundance and activity over AOB (Bowatte et al. 2009; Di et al. 2010). In this study pH was found to be a critical factor in determining AOA community structurc, and it would appear that pH is also a key factor determining the dominance of AOA over AOB, given that AOB were not detected in soils with pH <5. The importance of pH in controlling the abundance and structure of both AOA and AOB communities was also recently noted by Boyle-Yarwood et al. (2008), Nicol et al. (2008), and Lehtovirta et al. (2009). Nicol et al. (2008) found that, over a pH range 4.9-7.5, AOA gene abundance decreased with increasing pH, whereas AOB gene abundance increased with increasing pH. They also found that community structure of both AOA and AOB varies with pH. This finding was supported by Lehtovirta et al. (2009), who showed that the Group 1.1 Crenarchaeota AOA gene abundance increased with decreasing pH despite there being no change in the total AOA gene abundance.

While it is there is a clear trend of increasing AOA abundance with decreasing pH within each farm site, there is also a clear difference between the two sites. Figure 2 shows that although thc AOA abundances in the Hanks samples increase with decreasing pH, they do not fall on the same line as the samples from the other sites. The Hanks sample site is on a different property from the other three sample sites. This result suggests that some factor other than pH differs between the two properties, and may also be impacting on the AOA abundance.

Ammonia oxidation in acidic soils is a phenomenon that is difficult to reconcile with the known behaviour of isolated AOB, which fail to grow at pH below neutral (Frijlink et al. 1992). Explanations including the existence of acid-tolerant species or strains and the survival of AOB in alkaline micro-environments within an overall acidic soil have been offered to describe this phenomenon (Allison and Prosser 1993; De Boer and Kowalchuk 2001). A more plausible explanation is that AOA dominate nitrification in acidic soil, albeit at lower rates as shown here.

As noted earlier, N[H.sub.4.sup.+] availability is considered an important determinant of the structure of the ammonia-oxidising community (Ergudcr et al. 2009). There is evidence that the marine AOA Candidatus Nitrosopumilus maritimus, which is adapted to live under extreme N limitation, has significantly greater affinity for N[H.sub.4.sup.+]than noted for AOB (Martens-Habbena et al. 2009). However, N[H.sub.4.sup.+] availability was not closely correlated with ammonia-oxidiser community structure in our study, possibly because the acidic condition of soils was the major determinant of ammonia-oxidiser community structure. The poor correlation between AOA gene abundance and PNR may also be due to substrate inhibition. Since the traditional PNR test is undertaken following the addition of ammonium, it is conceivable that the PNR test favours AOB growth and not AOA growth. So, in the samples where gcne counts were high, it is possible that transcription of these genes was low under the conditions used for the PNR measurements. For example, Di et al. (2010) found that, while AOB grew well upon addition of animal urine to soils that elevated N[H.sub.4.sup.+] levels, growth of AOA was greatest in the control incubations without urine addition. Soil pH in the Diet al. (2010) study was consistently >5.5, indicating that pH was less likely to control community structure and N[H.sub.4.sup.+] supply was a more important factor.


The data reported here indicate that archaea are important members of the ammonia-oxidising community in the Australian soils studied. AOA are favoured by a different set of environmental conditions than the AOB traditionally considered to be the prime agents of ammonia oxidation in agricultural soils. The failure to detect AOB in soils of pH <5 indicates that soil pH is an important determinant of ammoniaoxidiser community structure. The presence of AOA in soil irrespective of pH, together with their reputed high affinity for ammonia, provides an explanation for nitrification in acidic, low fertility soils that are

widespread in many regions of Australia. Further work is required to ascertain the contribution of AOA to the nitrification process across a wide range of soils and agricultural systems. 10.1071/SR11288


The authors sincerely thank Dale Hanks and Alcoa Farmlands manager, Tony Hiscock, for allowing access to their properties, providing information about their land management practices, and for their assistance during sample collection. Sean Forrester (both CSIRO Land and Water, Adelaide) conducted MIR analysis and predictions.

Received 17 March 2011, accepted 4 November 2011, published online 6 January 2012


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Cathryn A. O'Sullivan (A,D), Steven A. Wakelin (B,C), lan R.P. Fillery (A), Adrienne L. Gregg (B), and Margaret M. Roper (A)

(A) CSIRO plant Industry, Private Bag 5, Wembley, WA 6913, Australia.

(B) CSIRO Land and Water, PMB 2, Glen Osmond, SA 5064, Australia.

(C) AgResearch Ltd, Lincoln Science Centre, Private Bag 4749, Christchurch 8140, New Zealand.

(D) Corresponding author. Email:
Table 1. Soil physical and chemical data

LOI, Loss on ignition (volatile solids). H, Hanks site; S, Stage 3;
B, Blakes; D, Driveway; numbers indicate depth in centimetres at the
bottom of the sample core. Variance given as standard error of the
mean from analysis of triplicate field samples. Estimates of variation
are not supplied for data measured from compounded samples or where
field replicates were not collected

Sample   Sample         pH in          Water content        LOI
         depth       Ca[Cl.sub.2]      (kg [H.sub.2]0/kg   (% dry
          (cm)          (1 :5)         wet soil)           soil)

H2        0-2     5.84 [+ or -] 0.06         0.43           22.35
H5        2-5     5.79 [+ or -] 0.10         0.40           20.79
H10       5-10    5.66 [+ or -] 0.15         0.26           12.19
S2        0-2     4.99 [+ or -] 2.72         0.62           74.87
S5        2-5     4.47 [+ or -] 0.15         0.39           25.38
S10       5-10    4.45 [+ or -] 0.01         0.19            9.98
B2        0-2     4.53 [+ or -] 0.13         0.41           38.05
B5        2-5     4.25 [+ or -] 0.03         0.17            7.99
B10       5-10    4.16 [+ or -] 0.01         0.11            4.21
D2        0-2     4.83 [+ or -] 0.04         0.12           42.67
D5        2-5     4.49 [+ or -] 0.03         0.07           10.75
D10       5-10    4.47 [+ or -] 0.05         0.05            5.20

Sample   Total N (A)      N[H.sub.4.sup.+]-N
             M)        (KCI [+ or -] extractable)
                            (mg/kg dry soil)

H2           0.6           14.2 [+ or -] 1.26
H5           0.6            7.8 [+ or -] 0.30
H10          0.3            6.4 [+ or -] 0.05
S2           1.0           23.3 [+ or -] 2.72
S5           0.7           16.2 [+ or -] 0.45
S10          0.2            5.2 [+ or -] 0.27
B2           0.8           34.6 [+ or -] 1.55
B5           0.2           1.38 [+ or -] 8.59
B10          0.1            2.1 [+ or -] 0.14
D2           0.4           35.2 [+ or -] 0.76
D5           0.2           23.6 [+ or -] 1.05
D10          0.2           12.3 [+ or -] 0.79

Sample   N[0.sub.3] -N         Total org. C   Clay (A)   Silt (A)

H2        23.4 [+ or -] 0.37       7.5          19.8       33.0
H5        14.7 [+ or -] 0.37       6.9          18.4       32.2
H10        6.0 [+ or -] 0.38       4.6          14.2       29.6
S2         3.9 [+ or -] 0.46       8.0           5.8       94.2
S5         2.4 [+ or -] 0.05       8.3          20.3       32.2
S10        1.7 [+ or -] 0.09       3.1          18.6       24.5
B2         3.0 [+ or -] 0.32      10.7           7.5       29.9
B5         4.3 [+ or -] 0.67       3.1           7.3       14.5
B10        1.7 [+ or -] 0.08       2.3           6.4       16.0
D2         4.9 [+ or -] 0.10       6.2           7.4       26.2
D5         4.5 [+ or -] 1.30       4.2           6.6       23.8
D10        3.4 [+ or -] 0.34       2.1           3.9       15.9

Sample   Sand (A)

H2         47.2
H5         49.4
H10        56.3
S2         0.0
S5         47.5
S10        56.9
B2         62.7
B5         78.3
B10        77.5
D2         66.4
D5         69.6
D10        80.2

Predicted using mid [+ or -] infrared analysis (Forrester et al. 2003).
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Author:O'Sullivan, Cathryn A.; Wakelin, Steven A.; Fillery, Ian R.P.; Gregg, Adrienne L.; Roper, Margaret M
Publication:Soil Research
Article Type:Report
Geographic Code:8AUST
Date:Nov 1, 2011
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