• prescribed burning;
  • forest soil;
  • laccase;
  • soil organic matter;
  • fungi


  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Repeated prescribed burning alters the biologically labile fraction of nutrients and carbon of soil organic matter (SOM). Using a long-term (30 years) repeated burning experiment where burning has been carried out at a 2- or 4-year frequency, we analysed the effect of prescribed burning on gross potential C turnover rates and phenol oxidase activity in relation to shifts in SOM composition as observed using Fourier-transform infrared spectroscopy. In tandem, we assessed the genetic diversity of basidiomycete laccases. While the overall effect of burning was a decline in phenol oxidase activity, Shannon diversity and evenness of laccases was significantly higher in burned sites. Co-correspondence analysis of SOM composition and laccase operational taxonomic unit frequency data also suggested a strong correlation. While this correlation could indicate that the observed increase in laccase genetic diversity due to burning is due to increased resource diversity, a temporal replacement of the most abundant members of the assembly by an otherwise dormant pool of fungi cannot be excluded. As such, our results fit the intermediate disturbance hypothesis. Effects were stronger in plots burned in 2-year rotations, suggesting that the 4-year burn frequency may be a more sustainable practice to ensure the long-term stability of C cycling in such ecosystems.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Fires have the capacity to consume a third of all net primary production in some ecosystems (Hicke et al., 2003) and hence can act as a major driver of ecosystem processes in soils. Controlled burning is a commonly implemented management strategy, which aims to reduce the fuel load by small-scale and low-intensity burning in order to reduce the risk of large and intense wildfires that can impact vast areas of land. Controlled burns are not generally stand-replacing, and hence such intervention must be carried out periodically. The immediate result of burning is a loss of oxygen-containing functional groups in the soil organic matter (SOM) with relatively labile plant carbon (C) (e.g. cellulose) generally being the most affected pool (Fernandez et al., 1997; Knicker, 2007). Effects on humic and fulvic acids include transformation of peripheral aliphatic chains and a general increase in aromaticity (Almendros et al., 1990, 1992). Essentially, prescribed burning both accelerates the humification process of organic matter and produces varying quantities of charred material. Char, also called ‘black carbon’, is generally thought to be highly recalcitrant to decomposition (Certini, 2005; Preston & Schmidt, 2006), although thermally altered material produced by low-grade fires is still biologically available (Baldock & Smernik, 2002) and even coal and lignite can be biologically decomposed (Wondrack et al., 1989; Fakoussa & Hofrichter, 1999; Rumpel & Kögel-Knabner, 2004).

Similarly, effects on soil nitrogen (N) pools vary with fire frequency and severity. While high-intensity, infrequent, wildfires generally generate a short-term pulse of available mineral N (mostly as NH4+, Certini, 2005), controlled burning eventually decreases total extractable mineral N pools (Hossain et al., 1995; Guinto et al., 1998, 1999) as well as levels of N mineralization (Guinto et al., 1998, 1999; Bastias et al., 2006a). Decreased N mineralization may be caused by increased recalcitrance of the organic N due to transformation into heteroaromatic N structures (Almendros et al., 2003; Knicker et al., 2005). Increases in soil pH can also occur with frequent burning, although often temporarily, as a result of organic acid denaturation (Certini, 2005). As a consequence, the soil microbial community structure in burned sites has generally been observed to undergo significant changes (e.g. Hart et al., 2005; Esquilin et al., 2007), although the reported effects are variable in relation to fire intensity and frequency (Chen & Cairney, 2002).

Although the immediate effect of fire on C stocks can be estimated, for example by calculation of the loss through volatilization, the longer-term effects on C cycling in fire-affected ecosystems are less well understood. Increased recalcitrance of SOM due to thermal alteration may have long-term consequences for primary production as N pools may be less bioavailable, and similarly for total C budgets, as decomposition processes may be affected. Structurally, more complex plant residues are generally degraded in relatively specialized enzymatic reactions, usually involving extracellularly produced oxidative enzymes. Of these, phenol oxidases, and particularly laccases, are generally the most characterized. The involvement of phenol oxidases, including laccases, in lignin decomposition is now generally accepted. Assigning a specific role to, for example, laccase in decomposition in environmental samples is, however, difficult due to overlapping substrate specificities and possible co-operation with other oxidative enzymes (Thurston, 1994; Leonowicz et al., 2001). A role in lignin decomposition for laccases is thus far exclusively attributed to the extracellularly produced laccases of the fungal kingdom, specifically those produced by wood-rotting fungi and several litter-decomposing saprotrophs (Leonowicz et al., 2001; Baldrian, 2006).

Various effects of long-term, repeated, and low-intensity burning have been characterized in wet sclerophyll forest at Peachester State Forest, Australia, where experimental burning has been carried out since 1972. Previous observations at this site have reported significant reductions in the relative proportion of alkoxy/carbohydrate C of the SOM as well as a decrease in total C and N stocks and N mineralization rates (Guinto et al., 1998, 1999; Bastias et al., 2006a, b). In addition, Anderson et al. (2007) showed that significant shifts had also occurred in the general fungal as well as ectomycorrhizal community composition at these sites. We, therefore, analysed the activity of phenol oxidase enzymes and, specifically, the diversity of basidiomycete laccase-encoding genes to evaluate the hypothesis that the previously observed changes in fungal community structure due to the fire management are reflected at a functional level, with the hypothesis that fire-affected plots would be characterized by more recalcitrant organic matter and consequently higher diversity within the laccase-encoding gene pool.

Materials and methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Site description and sampling

Peachester State Forest (Queensland, Australia, 26°50′S, 152°53′E) is a wet sclerophyll forest, dominated by Eucalyptus pilularis Smith. The experiment is arranged as three blocks, each containing two plots (30 × 27 m or 40 × 20 m) of each burning treatment (18 plots in total; six of each treatment). Burning frequencies are 2 or 4 years, while there are also unburned (since 1972) control sites. Burn intensities have been variable yet generally of low intensity (<500 kW m−1, Guinto et al., 1999). The soil types at this experimental site are classified as yellow to red Kandosols, although block 3 appears to be situated on a slightly different soil type, characterized by considerably lower organic matter content and a sandier texture (Guinto et al., 1998).

Core samples (2.5 cm diameter × 20 cm length) were taken at 10 random locations within each treatment replicate plot, in late spring (October) 2006 (14 months after the most recent burn, which was carried out at both the 2-year and the 4-year frequency plots), immediately separated into 0–10 cm and 10–20 cm depths, and the 10 plot replicates for each horizon were pooled to encompass spatial variability within each plot. After transfer to the laboratory on ice and overnight storage at 4 °C, the resulting 36 samples were sieved to 2 mm (<5 min per sample). Subsamples for analysis of genetic laccase diversity were stored frozen at −80 °C immediately after sieving. Samples for fourier-transform infrared spectroscopy (FTIR) analysis were freeze dried within 3 days and samples for enzyme analysis and potential C turnover were used following storage at 4 °C for a maximum of 2 weeks. Fungal biomass estimation by extraction of phospholipids fatty acids (PLFA) was performed on freeze-dried samples.

Phenol oxidase activity

Phenol oxidase activity was tested by incubation of 0.5 mL of a soil slurry (1 g soil homogenized with 5 mL of 50 mM citrate–phosphate buffer, pH 4.5) with 3 mM 2,2′-azino-bis-(3-ethylbenzothiazoline-6-sulphonic acid) (ABTS) in citrate–phosphate buffer at 25 °C for 30 min (Luis et al., 2005b). Although this assay is not specific for phenol oxidases (ABTS is a generic oxidase substrate), it was used to conform to other studies of phenol oxidase activity in fungal cultures and soil (e.g. Günther et al., 1998; Min et al., 2001) as 3,4-dihydroxy-l-phenylalanine could not be used due to interference of the soil slurry colour with the quinine formed during this reaction (not shown). Phenol oxidase activity was determined after centrifugation of the sample at 9000 g for 1 min and measuring A420 nm. The measured absorbances were converted into enzymatic activity expressed as turnover of ABTS (1 U=1 μmol min−1) using the Beer–Lambert law with the extinction coefficient for oxidized ABTS (36 000 M−1 cm−1; Luis et al., 2005b). Activity was expressed as 1 × 10−2 U g−1 dry soil or as a function of fungal biomass.

Fungal biomass estimation by PLFA analysis

Lipid extraction and PLFA analyses were performed as described by Frostegåård et al. (1993) using the modified Bligh & Dyer (1958) method. Briefly, 500 mg of milled, freeze-dried soil was extracted with a chloroform–methanol–citrate buffer mixture (1 : 2 : 0.8), and phospholipids were separated from other lipids on a silicic acid column before mild-alkali methanolysis. The resulting fatty acid methyl esters were resolved by GC. Specific modifications and GC conditions were as described by Certini et al. (2004). The PLFA 18 : 2ω6,9 was taken to indicate predominantly fungal biomass (Frostegåård et al., 1993). Data were expressed as nmol g−1 soil (dry weight).

FTIR spectroscopy

Spectral characterization of the SOM was performed by diamond attenuated total reflectance (DATR) FTIR spectroscopy using a Nicolet Magna-IR 550 FTIR spectrometer (Thermo Electron, Warwick, UK) fitted with a potassium bromide beam splitter and a deutroglycine sulphate detector. A DATR accessory, with a single reflectance system, was used to produce transmission-like spectra. Samples were dehydrated by freeze drying and powdered by ball milling with zirconium balls. Samples were placed directly on a DATR/KRS-5 crystal and a flat tip powder press was used to achieve even distribution and contact. Spectra were acquired by averaging 200 scans at 4 cm−1 resolution over the range 4000–350 cm−1. A correction was made to spectra for the ATR to allow for differences in the depth of beam penetration at different wavelengths (omnic software, version 7.2, Thermo Electron). All spectra were also corrected for attenuation by water vapour and CO2. Minor differences in the amplitude and baseline between runs were corrected by normalization of the data by subtraction of the sample minimum followed by division by the average of all data points per sample. FTIR spectral data in the diamond interference region (2200–1900 cm−1) were excluded from statistical analyses.

Fungal laccase genetic diversity

Laccase genetic diversity was determined using basidiomycete laccase-specific clone libraries. Total nucleic acid was extracted from 0.5 g soil using a modified CTAB-PEG6000 extraction (Anderson et al., 2003) and resuspended in 50 μL ultrapure water. PCR was performed subsequently using a previously published basidiomycete-specific laccase primer pair (lccF/lccR, D'Souza et al., 1996). Reactions (50 μL) contained 2.5 U Taq polymerase and associated polymerase buffer (Bioline, UK), 2.0 mM MgCl2, 250 μM of each dNTP, 0.4 mg mL−1 bovine serum albumin (Roche, UK), 20 pmol of each primer and c. 50 ng of template DNA. Thermal cycling was carried out using the following cycling conditions for basidiomycete-specific laccases: 94 °C for 5 min, then 35 cycles of 94 °C for 1 min, 54 °C for 2 min and 72 °C for 5 min, followed by a final extension step at 72 °C for 10 min (D'Souza et al., 1996). A 1/10th volume of the reactions was used to visualize amplicons on 2.5% agarose gels before purification using the ChargeSwitch PCR Clean-Up kit (Invitrogen, UK). Clone libraries were prepared from pooled PCR products (containing equal volumes of each of the PCR products from the six replicates) for each burning treatment × horizon combination (i.e. n=6 clone libraries) to minimize sequencing cost. The pooled laccase fragments were cloned into Escherichia coli JM109 using the pGem-T Easy System I cloning kit (Promega, UK) using a vector : insert ratio of 3 : 1. White transformants were selected for amplification of the cloned laccase fragment using vector-based M13 primers. Any transformants containing inserts between c. 180 and c. 400 bp were subjected to sequence analysis using the vector primers T7 and SP6. Sequences were determined on an abi 3130xl using abi v3.1 chemistry. Forward and reverse sequences were assembled using sequencher 4.2 (GeneCodes Corporation) and trimmed to exclude vector ends and Taq polymerase overhangs. Resulting consensus sequences were queried against GenBank using blastn (Altschul et al., 1990). Determination of intron and exon boundaries was carried out by multiple alignment with laccase sequences from type strains where similarity to known laccase genes was high. Where homology to previously described laccase genes was low, visual inspection for verified 5′ and 3′ splice sites and corresponding branch sites (Kupfer et al., 2004) was used to determine putative protein-coding regions. All laccase sequence data were submitted with annotated features to GenBank under accession numbers EU277750277779, EU332168332306 and EU360802809.

To enable analysis of the distribution of laccase sequence types in the treatments and soil horizons, operational taxonomic units (OTU) were defined as groups of sequences (excluding sequences with mutations in splice sites or exons) sharing 100% similarity in predicted amino acid sequences. These were calculated using sequence identity matrices of deduced amino acid sequences within bioedit. Rarefaction curves were produced using the software analytic rarefaction (S. Holland, using intervals of 1 to generate confidence limits. The frequency distribution of OTU types in the clone libraries was used for further statistical analysis as described below. Diversity estimates were carried out using the software estimates, version 7.5 (Colwell, 2005).

Phylogenetic analyses

The deduced coding regions were subjected to blastx searches to query for related protein sequences and an amino acid based alignment produced using clustalw in bioedit (v7.0.5.2, Hall, 1999) including amino acid sequences of the retrieved nearest neighbours. Phylogenetic analyses of the partial predicted amino acid sequences of the clones and related laccases with tested physiological functions (Hoegger et al., 2006) were conducted in mega4 (Tamura et al., 2007). Neighbour-joining trees (Saitou & Nei, 1987) were constructed using three different distance estimation models (p-distances, Dayhoff/PAM, JTT), and tree topology was tested using bootstrapping with 999 replicates (Felsenstein, 1985). Further evaluation was performed using bootstrapped analysis using the maximum parsimony method with the Jones, Taylor and Thornton model for amino acid substitution (Jones et al., 1992). Evolutionary distances in the presented tree are in the units of the number of amino acid substitutions per site. All positions containing gaps and missing data were eliminated from the dataset (complete deletion option). There were a total of 46 positions in the final dataset. Related sequences included in the final analysis were published laccase sequences from Acl, Ampulloclitocybe clavipes; Led, Lentinula edodes; Mga, Mycena galopus; Pci, Pleurotus ciliatus; Pco, Pleurotus cornucopiae; Pos, Pleurotus ostreatus; Tpu, Trametes pubescens; Tve, Trametes versicolor; Tvi, Trametes villosa. The second major, and well-supported, branch contains ascomycete laccases from Bci, Botryotinia fuckeliana; Ggg, Gaeumannomyces graminis var. graminis; NcrP, Neurospora crassa; and Vco, Verpa conica.

Statistical analyses

The effects of the burning regime on phenol oxidase activity (per gram soil or per nanomole fungal biomass), fungal biomass and C mineralization potential were analysed using general linear models, coding the nested structure of the soil horizon sampling within plots of different burning regimes and including the block structure in the model. The effects of burning regime and soil horizon on SOM quality was established using a canonical variate analysis after dimension-reducing principal components analysis (PCA-CVA) using the FTIR wavenumber normalized data. The first three principal component (PC) scores (including a total of 87.7% of the original variance) of the FTIR dataset were used in the CVA. Significance of the CVA groupings was established by permutation analysis (1000 repetitions of Monte Carlo permutations). This test was not possible for the laccase OTU frequency data as there was no residual replication for the factors due to the pooling of replicates for clone library construction. The effect of different wavebands in the FTIR spectra (i.e. specific components of the OM structure) on separation of samples by burning regime or horizon was investigated using loadings plots of the first two PCs.

Finally, to investigate whether there was correlation between the separation of samples by burning regime and/or soil horizon in terms of SOM composition and laccase OTU frequency distribution, we compared the two datasets using predictive co-correspondence analysis (CO-CA) (Ter Braak & Schaffers, 2004). CO-CA maximizes the weighted covariance between weighted averaged species scores of one community and weighted averaged species scores of the other community. It thus attempts to identify the patterns that are common to both communities. To ensure that data were matched correctly, the averages of the six replicates for each burn × horizon combination for the FTIR dataset were used in CO-CA to match the laccase OTU frequency distribution data, as these were gathered from pooled amplicons of the original six replicate soil samples. Permutation testing (n=999) was used to assess the significance of the ordination axes. Because of the pooling of amplicons for the laccase clone libraries, we were unable to perform this analysis without the samples from the different soil type in block 3.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Phenol oxidase activity in relation to fungal biomass and potential C mineralization rate

Phenol oxidase activity was detected in all but two of the 36 samples. There was no significant effect of the sampling horizons on phenol oxidase activity when converted to either soil dry weight or total fungal biomass (fungal kingdom-specific PLFA) equivalents (P=0.250, Fig. 1a, and P=0.578, Fig. 1b, respectively, when analysed using general linear models). With both ways of expressing activity, burning on a 2-year rotation significantly decreased phenol oxidase activity (P=0.012 and P=0.037, respectively; Fig. 1a and b). The observed trend for a decline in phenol oxidase activity between 4- and 2-year burning frequencies (Fig. 1a and b) was not significant. There was no overall effect of the burning treatment on C mineralization rates or total fungal biomass (as assessed by PLFA); however, there was a significant effect at the horizon level in the 4-year burning frequency plots (Fig. 1c and d). If we excluded samples from block 3 in repeat statistical analyses, as this block appeared to be based on a different soil type (see Site description and sampling for details), it did not alter these results (not shown).


Figure 1.  (a, b) Phenol oxidase activity, (c) potential C turnover rates and (d) fungal PLFA in the top 0–10 cm (filled bars) and subsurface 10–20 cm (open bars) of experimental burning treatments. (a) Data expressed per gram of dry soil; (b) data expressed per nmole of fungal PLFA biomarker in the soil. Data shown are the mean of n=6 ± SEM and bars sharing a letter are not significantly different in two-way anova. Letters directly above bars (a, ab and b) represent significance at the horizon-within-burn stratum and letters above brackets (x, xy and y) indicate significance at the ‘burn’ stratum.

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SOM composition

Analysis of the SOM composition by FTIR spectroscopy showed no significant overall differences according to the burning treatments in PCA-CVA when data from all six sampling plots were included, although there was a strong trend of separation according to burning regime (Fig. 2a and c). As the samples from block 3 originated from a sandier soil type, it is possible that the FTIR signals of the soil minerals interact with the organic matter signatures (e.g. Parker, 1971), which could effectively mask the effects of burning on SOM composition; we therefore repeated the statistical analysis excluding block 3 samples from statistical analysis. In this case, with only the four replicates from blocks 1 and 2, the burning treatments showed statistically significant (P<0.05) effects on sample separation in PCA-CVA in both surface and subsurface soils (Fig. 2b and d). The effect of the burning regime was predominantly a separation of the groups in the first dimension of PCA-CVA. The loadings plots (not shown) indicated that separation in the first dimension was associated with strong negative loadings in the waveband envelope around 1100 cm−1. This region reports on vibrations resulting from the combination of C–O stretching and O–H deformation, and is generally ascribed to carbohydrate signatures (Parker, 1971; Grube et al., 2006). This is precisely the region where mineral interference occurs, explaining why the effect of burning was less strong when all samples were included.


Figure 2.  Canonical variate analysis of organic matter composition using the first three dimensions (containing 87% of original data variance) from a variate reducing PCA of FTIR data. (a, b) The means ± SEM of the control (circles), 2-year rotation (triangles) and 4-year rotation (squares) burning treatments in the surface soils for all six replicates, and omitting samples from block 3 replicates (i.e. n=4), respectively. (c, d) The means ± SEM for the subsurface horizons, using all six replicate plots and omitting block 3 replicates, respectively.

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Laccase diversity

Basidiomycete-specific laccase sequences were successfully amplified from all DNA extracts. A total of 206 clones were analysed by sequence analysis. Of these clones, eight sequences were significantly longer than the average sequence length (200 bp) and, although the nearest neighbours to these sequences were all laccases or laccase-like multicopper oxidases (data not shown, EU360802306809), five of these sequences carried a frameshift mutation in exon 3, which would prevent correct translation. We, therefore, excluded these sequences from further analysis. One further clone sequence of average length had a mutation in exon 2 to a stop codon that would result in a nonfunctional protein and was also excluded. A further 35 clone sequences had mutations in the splice sites (77% of these in the 5′ splice site and 23% in the 3′ splice site) that could prevent correct translation. Although not used in subsequent analysis, some of these sequences shared 100% amino acid similarity to other sequences over the coding region if the mutation in the splice site was ignored (data not shown). Such splice site mutations were more frequent in burnt plots. The coding regions of the remaining 165 laccase sequences were translated into amino acid sequences and formed a total of 95 OTU types based on 100% amino acid similarity. OTU types with more than one occurrence are shown in Table 1. Rarefaction analysis of laccase OTU type abundance (Fig. 3) indicated greater genetic diversity of laccases in plots burned with a 2- or 4-year frequency vs. the unburned control in both the surface (Fig. 3a) and in the subsurface soils for the 2-year frequency (Fig. 3b) at the lowest common sampling effort. The rarefaction curves of the control sites also show a tendency to approach a plateau. Diversity estimates (Table 2) also returned higher Shannon indices as well as evenness in the burned plots.

Table 1.   Nearest neighbour for coding sequence of laccase OTU types occurring more than once in the clone libraries and distribution in the surface and subsurface horizons of control (unburned) and burned (at 2- and 4-year frequency) forest soil
OTU typeAccession numberTotal # of occurrencesNearest neighbour by amino acid similarity and nucleotide similarity, respectively*Occurrence in clone libraries
Control (0–10)2 years (0–10)4 years (0–10)Control (10–20)2 years (10–20)4 years (10–20)
  • *

    Figures in angular brackets denote accession numbers, parentheses following accession numbers denote % homology followed by % sequence overlap to this accession. MCO, multicopper oxidase.

1EU33224221Russula atropurpurea laccase [CAD65816] (91/100)1310520
  Uncultured fungal putative laccase [EF117026] (85/84)      
44EU3322226Putative laccase [CAF24987] (100/100)002022
  Uncultured basidiomycete lac gene [AJ626701] (94/84)      
3EU3321695Uncultured fungal putative laccase [ABM73929] (93/100)012002
  Uncultured fungal laccase [EF117184] (100/97)      
10EU3321784Putative laccase [ABM73780] (95/100)010210
  Uncultured fungal putative laccase [EF117026] (88/84)      
12EU3321803Putative laccase [ABM73777] (95/100)021000
  Uncultured fungal laccase [EF117156] (92/84)      
25EU3321973Putative laccase [ABM73903] (82/100)020010
  Uncultured fungal laccase [EF117156] (82/84)      
28EU3322013Putative laccase [CAD12473] (82/100)003000
  Uncultured basidiomycete lac gene [AJ420346] (79/72)      
45EU3322253Polyporus brumalis LAC1 [ABN13591] (95/100)002100
  Panus tigrinus partial lac2 mRNA [AM419159] (85/100)      
56EU2277693Putative laccase [CAD62557] (95/100)100101
  Uncultured basidiomycete lac gene [AJ540294] (89/84)      
60EU3322433Lactarius blennius laccase [CAD65841] (82/100)000030
  Uncultured fungal laccase [EF117184] (79/84)      
5EU3321722Putative laccase-like MCO* [ABN72307] (93/100)010010
  Uncultured basidiomycete lac gene [AJ626702] (89/84)      
7EU3321752Putative laccase [ABN73929] (93/100)020000
  Uncultured fungal putative laccase [EF117184] (90/84)      
16EU3321862Putative laccase [ABN73939] (82/100)020000
  Verpa conica partial lac gene [AJ15433] 100/98      
18EU3321892Putative laccase [ABN73798] (97/100)010010
  Uncultured fungal putative laccase [EF117065] (94/86)      
24EU3321962Putative laccase-like MCO [ABN72309] (91/100)010010
  Uncultured fungal putative laccase [EF117041] (85/84)      
30EU3322032Putative laccase [CAF24987] (97/100)101000
   Uncultured fungal putative laccase [AJ626701] (90/84)      
37EU3322122Putative laccase [CAF24987] (85/100)001001
  Uncultured fungal putative laccase [AJ626701] (88/84)      
52EU2777592Putative laccase-like MCO [ABN72307] (89/100)200000
  Uncultured fungal putative laccase [EF117019] (85/84)      
78EU3322802Putative laccase [CAF24987] (95/100)000020
  Uncultured fungal putative laccase [AJ626701] (85/84)      
79EU3322822Putative laccase [ABN73888] (74/100)000020
  Uncultured fungal putative laccase [EF117026] (79/84)      
80EU3322882Lactarius blennius laccase [CAD65841] (76/100)000002
  Lactarius blennius partial lac gene [AJ542649] (75/84)      
82EU3322892Putative laccase-like MCO [ABN72309] (93/100)000002
  Uncultured fungal putative laccase [EF423204] (86/84)      
85EU3322952Putative laccase-like MCO [ABN72307] (89/100)000002
  Uncultured fungal putative laccase [EF117184] (89/84)      

Figure 3.  Rarefaction curve analysis of the laccase OTU type abundance from clone libraries constructed from pooled (n=6) basidiomycete-specific laccase PCR amplicons from whole soil DNA. (a) Diversity in the surface soils (0–10 cm, closed symbols) and (b) diversity in the subsurface (10–20 cm, open symbols) soils. Codes are as follows: controls (circles), 2-year burn frequency (triangles) and 4-year burn frequency (squares). Error bars shown are 95% confidence limits.

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Table 2.   Diversity measures of laccase genes in the experimental burning treatments at Peachester State Forest
Soil depth (cm)Burn frequencyShannon (H′) indexShannon evenness (J′)
2 years3.090.99
4 years2.760.97
2 years3.060.91
4 years2.340.91

Distribution of laccase OTU types and phylogenetic analysis

While most OTU occurred at most two or three times, four sequence types were found four, five, six and 21 times (Table 1). OTU type 1, which showed some amino acid homology to laccase from Russula atropurpurea, occurred 21 times in the libraries. This OTU type was not found in the 4-year burn plots and was much less frequently observed in the 2-year burn plots; yet it was the most abundant OTU in the controls. A large number of the OTU types with multiple occurrences of lower frequency were found in burned plots, but not in the controls. These laccases had only low homology to previously characterized laccases, with most hits generated against other soil-derived laccase sequences of unconfirmed function (Table 1). With very few exceptions, the amino acid sequences of our clones concurred with the L1 and the start of the L2 signature sequences identified by Kumar et al. (2003) for the copper-ligating regions of fungal laccases. To try and gain a better understanding of the potential role of these laccases, we analysed alignments of the predicted amino acid sequences of all observed laccase OTU together with laccases of previously characterized functions. Hoegger et al. (2006) presented such a phylogenetic analysis of over 350 characterized, full-length, multicopper oxidases and concluded that the gene phylogeny partitioned at least partly according to the function of the enzymes. In their study, sequences for characterized laccases from wood-decaying or litter-decomposing fungi were clustered separately from laccase genes with a characterized function in, for example, plant pathogenic fungi or those involved in pigmentation. Therefore, we investigated whether, using our partial sequences, the observed laccase OTU grouped with other genes with known function in lignin or litter decomposition. The resulting overall tree topology from all analyses concurred with the phylogenetic analysis of fungal laccases conducted by Hoegger et al. (2006) (not shown); although confidence in the branching in the data was relatively low, partly due to the short fragment length and presumably also partly due to the use of this relatively conserved region. We present a reduced tree in Fig. 4, which shows the well-supported branch for previously reported ascomycete laccase sequences (Fig. 4). With the exception of the singleton OTU 46 and 91, which grouped with multicopper oxidase amino acid sequences that may be implicated in hyphal pigmentation (not included in final phylogenetic analysis), all cloned laccase sequences from the Peachester forest soil clustered with basidiomycete laccase sequences that were reported previously by Hoegger et al. (2006) to form a well-supported group when a larger fragment of the laccase gene sequences was used in phylogenetic analyses. This is illustrated with a partial neighbour-joining tree constructed using a representative subset of observed OTU types (Fig. 4). Many of the neighbouring basidiomycete laccase sequences originate from fungi where their function in lignin or litter decomposition has been recognized previously (reviewed in Hoegger et al., 2006).


Figure 4.  Phylogenetic reconstruction of cloned laccase sequences from forest soil subjected to experimental prescribed burning (excluding sequences with mutations) and related fungal laccases, based on amino acid sequences. The evolutionary history was inferred using the neighbour-joining method, with distances computed using the Jones, Taylor and Thornton matrix-based method. The optimal tree with the sum of branch length=4.95333022 is shown. The percentage of replicate trees in which the associated taxa clustered together in the bootstrap test (999 replicates) is shown next to the branches. The tree is drawn to scale. Accession numbers are given in brackets, followed by gene designation and preceded by species codes as detailed in the Materials and methods.

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Relationship of laccase OTU distribution due to burning regime and soil horizon with SOM composition of matched samples

To test our hypothesis that increased SOM recalcitrance is reflected by increased laccase genetic diversity, we tested for correlation between the two datasets. CO-CA allows statistical correlations between two multivariate datasets without prior derivatization of one of the datasets to one or more explanatory axes, as often used, for example, in redundancy analysis using the first few PCs of the explanatory dataset (Ter Braak & Schaffers, 2004). Such indirect approaches work well if the major pattern of variation in the explanatory dataset is important for the response community. This is, however, not always the case. CO-CA uses the reciprocal averaging approach to correspondence analysis and applies this to the separate species datasets and correlates the resulting ordination axes, thus identifying patterns that are common to both communities. Predictive CO-CA analysis using the FTIR spectroscopic signatures as the explanatory dataset indicated (Fig. 5, Table 3) that there is a statistically significant level of correlation between laccase genetic diversity and SOM composition in the sample separation caused by burning regime and the soil horizon, as also evidenced by the proximity of matched samples in Fig. 5. The significant correlations explained 35% of the overall fit between the two datasets (Table 3).


Figure 5.  Biplots showing site scores resulting from predictive co-correspondence analysis on the frequency distribution of observed laccase OTU (response variate) with the composition of the soil organic matter as analysed by FTIR spectroscopy (predictor variables). Filled symbols for surface samples (0–10 cm) and open symbols denote samples at 10–20 cm depths. The burning frequency is indicated by symbols (circle, control; triangle, 2-year frequency; and square, 4-year frequency). The correlation coefficients and variance explained by each axis can be found in Table 3.

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Table 3.   Correlation coefficients between the site scores of the average SOM composition (FTIR-derived) and fungal laccase OTU frequency in the different fire regime × soil depth samples, using predictive CO-CA
AxesCorrelation coefficientPercentage fitP-value


  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

It needs to be stressed first that the different measures of activity that were determined in this study have slight differences in their biological origins and hence some consideration needs to be given to the ecological interpretation of the data we have presented. Extracellular phenol oxidases are produced by many organisms including plants and bacteria (e.g. Kellner et al., 2008), although the main producers are fungi (Baldrian, 2006). In addition to the often-observed problems with amplification from soil samples such as enzyme inhibition due to residual soil phenolics or bias due to nonexhaustive DNA extraction techniques (Anderson & Cairney, 2004), our chosen laccase primers specifically targeted laccases from basidiomycetes. Further discrepancies may arise from laccase sequence types not covered by the primers we used. Luis et al. (2005a), for example, showed, for a less degenerate basidiomycete-specific primer set targeting the same region, that introns may occur in the binding sites of primers within the conserved regions that encode the copper-binding sites in the protein. In contrast, the fungal PLFA biomarker encompasses all fungal phyla (Frostegåård & Bååth, 1996), although there are differences in the amount of this biomarker that can be found in different fungi (e.g. Olsson et al., 2003). Therefore, a lack of correlation between some of the measures of activity we determined may simply be because the different techniques report on different aspects of the total pool of fungal biomass and/or phenol oxidase activities. We therefore discuss our results in isolation first.

In this study, we observed a decrease in total extracellular phenol oxidase activity in the burned sites. This is in direct contrast with our hypothesis as we would have expected the observed reduction in total available N and easily decomposable C sources to result in greater reliance on lignin-decomposing enzymes. Other studies that have characterized phenol oxidase activity in response to fire, for example Boerner & Brinkman (2003), Boerner et al. (2006), have observed a marked increase in phenol oxidase activity. However, there is also some evidence in the literature that phenol oxidase activity is not affected by fire (Boerner et al., 2000, 2005). Similarly, the responses of soil phenol oxidase activity to increased mineral N availability have been equivocal (e.g. Waldrop & Zak, 2006; Blackwood et al., 2007). Enzyme activities in situ are controlled by complex interactions between substrate availability, enzyme concentration, soil physics and thermodynamics. For example, the concentration of active enzymes within the soil matrix can be affected as enzymes can be stabilized or inhibited due to interaction with soil minerals (e.g. Nannipieri et al., 2002). The relative potential pool of phenol oxidases may also be reduced simply due to a reduction in fungal biomass caused by frequent burning, as such a reduction in fungal biomass was previously shown for the Peachester site by Campbell et al. (2008). In the current study, however, we did not observe an effect of fire frequency on total fungal biomass, and hence the decline in phenol oxidase activity was not simply explained by differences in total fungal biomass, although, as mentioned previously, an effect on particular parts of the fungal community would not have been detected using the PLFA method. The phenol oxidase test we used would also report on phenol oxidase activities as well as potentially other oxidases of other soil inhabitants and hence the net decline may also have been the result of declines in populations of other oxidase producing biota (e.g. bacteria, Kellner et al., 2008). The discrepancy in the effect of burning on fungal biomass with the results reported by Campbell et al. (2008) could arise due to differences in sampling time since the last fire event as well as differences in the residual effect of the last fire preceding sampling. It is therefore also possible that fire intensity, frequency (both in the short and long term) and time since the most recent fire event determine the response of total phenol oxidase activity.

Our basidiomycete-specific laccase results clearly showed, however, that fungal laccase markedly differs in the composition of the OTU types present and that evenness is increased in burned sites. There has been no prior genetic study of laccases in burned ecosystems, but three previous reports have tested the hypothesis that laccase abundance is lowered by additional N inputs through N deposition. The first two of these studies showed no response of phenol oxidase activity or the genetic diversity of laccases to N deposition (Lauber et al., 2009), but confirmed a strong response to the level of recalcitrance of the SOM (Blackwood et al., 2007). Hofmockel et al. (2007) reported decreased laccase gene abundance due to atmospheric N deposition in an oak-forest ecosystem, yet no effect on total soil phenol oxidase activity. We hypothesized that increases in laccase genetic diversity would be a result of an increased need to access the thermally altered SOM pool due to the decline in the total available N and the more easily available soil C pool (e.g. cellulose). Our FTIR spectroscopy data indeed supported a loss of labile C if the data from the different soil type was excluded from statistical analyses. In tandem, previous work at the Peachester site had indicated a decline in total available N (e.g. Guinto et al., 1999). It may be possible that the response of fungal laccase activity to fire is different for functionally different fungi and that this may partially depend on the effects of fire on soil C and N pools. Recent evidence, for example, suggests that the primary role of saprotrophic fungi during litter decomposition may be to mobilize C, while mycorrhizal fungi mobilize N (Hobbie & Horton, 2007). Many mycorrhizal and putatively mycorrhizal fungi have been shown to have the ability to produce laccases (Luis et al., 2004; Pointing et al., 2005; Kellner et al., 2007). In long-term prescribed burns, as demonstrated at Peachester, both soil total N and mineralizable N pools are eventually decreased due to the repeated cycle of a flush of mineral N immediately after a fire event followed by loss from the system (Hossain et al., 1995; Guinto et al., 1999). This could perhaps lead to enhanced survival or fire-regulated activation for those fungal species that possess laccase activity under such conditions (as, for example, through N-regulated laccase transcription in some basidiomycetes as demonstrated by Chen et al., 2003). Indirect support of this also comes from observed reductions in plant N uptake in long-term burned sites (Vance & Henderson, 1984), which could indicate increased competition for resources between fungi and plants. To our knowledge, there is no literature available that reports a direct link between laccase activity or presence in saprotrophic fungi and a proven competitive advantage for those fungi that have the ability to access thermally altered or otherwise rendered recalcitrant SOM in a soil environment. Some reports have, however, observed alterations of laccase transcription or activity for various saprotrophic fungi when grown in mixed culture (e.g. Hatvani et al., 2002; Baldrian, 2004). Previous nuclear magnetic resonance observations of Guinto et al. (1999) showed significant reductions in the relative proportion of alkoxy/carbohydrate C in the burnt plots at Peachester. The FTIR data obtained in our present work indicate a continuing loss of carbohydrate C, and hence long-term alteration of the gross SOM composition. The results of CO-CA analyses suggested a degree of correlation between the composition of the observed laccase genes and the SOM composition. Increased complexity of SOM due to accelerated humification caused by fire may thus command greater diversity of lignin-decomposing enzymes. It is therefore possible that our results are indicative of the traditional concept of resource partitioning (Chesson, 2000) within this soil environment, which would be enhanced after repeated fire events, as a competitive advantage would be conferred to those fungi that possess laccase activity. There are, however, a large number of laccase OTU types that occurred in the burned plots, but were not observed in the controls (Table 1) causing greater evenness (Table 2). Although we are unable to present a possible explanation for the increased frequency of sequences with mutations in the splice sites, these are also interesting to note in this context. The observed responses to burning within the laccase gene pool would fit current thinking within the framework of the intermediate disturbance hypothesis (Roxburgh et al., 2004), i.e. that disturbances that occur at frequencies lower than the generation time of the organisms studied create conditions that call upon latent (genetic) diversity within the population, for example dormant cells such as spores – this is termed the ‘storage effect’ (Chesson, 2000). As the laccase gene pools within each horizon or treatment type were not sampled to plateau, we cannot exclude the possibility that laccase genes emerge under fire treatment, but are present only as rare occurrences in control plots. The observed increase in evenness with burning points to a likely storage effect scenario.

Repeated burning can specifically reduce mycorrhizal fungal community diversity (Chen & Cairney, 2002; Hart et al., 2005; Tuininga & Dighton, 2004) and may favour microfungi, perhaps due to the increased nutrient availability and reduced C flow from plant hosts immediately after fire events (Cairney & Bastias, 2007). It has also been reported that prolific fruiting of ascomycetes, especially of the order Pezizales, occurs after burning (Wicklow, 1975; Zak & Wicklow, 1980; Fujimura et al., 2005). These fungi are thought to produce a wide array of hydrolytic and phenol-oxidizing capabilities (Egger, 1986). Indeed, Kellner et al. (2007) recently reported the presence of variably expressed laccase genes in members of the Morchellaceae, some of which are commonly reported to fruit en masse after forest fires (Duchesne & Weber, 1993; Pilz et al., 2004). Bastias et al. (2006b) examined the effects of prescribed burning on the ectomycorrhizal community at the Peachester site. Certain ectomycorrhizal groups such as Thelephoraceae were only present in the surface horizons of burned sites and increased in prevalence in the highest burn frequency while other groups such as Pisolithaceae and Cortinariaceae declined severely in the surface horizons of fire-affected plots. While there is evidence for the species within these groups for the production of extracellular laccases (e.g. Kanufre & Zancan, 1998), there is still a lack of sequence data in the public databases for genes coding for enzymes produced extracellularly by such fungi. This may have contributed to the lack of matches to characterized laccases.

Some fungi, including those mentioned in the previous paragraph, form highly pigmented hyphae, which may indicate a different function for some of the laccase OTU types we observed. Intracellularly expressed laccases have been associated with a variety of functions, including pigmentation, during mycelial or spore development, and are sometimes produced in concert with extracellular laccases (Baldrian, 2006). However, our phylogenetic analyses indicate that the majority of the laccases we obtained sequence data for in this study are probably unlikely to have a direct role in pigmentation. As we reported earlier in this manuscript, our phylogenetic analyses generally concurred with the comprehensive study of laccase diversity vs. function by Hoegger et al. (2006) and showed the nearest neighbours to the observed laccase sequences to be on the whole basidiomycete laccases with confirmed functions in litter or wood decay. Lack of good sequence matches with fungal laccases of known species or similarity to laccases from fungal species that were previously reported to be affected by the fire frequency at Peachester (Bastias et al., 2006b), however, limits further speculation at this point, and further biochemical characterization of these putative laccases would be required to confirm their specific function within the Peachester forest ecosystem.

In summary, our results show that, despite decreases in net phenol oxidase activity, prescribed burning in sclerophyll forest ecosystems causes a significant shift in the basidiomycete laccase-encoding gene pool, that evenness is increased and that the composition of laccase-encoding genes within this ecosystem is at least partly correlated with the changes in SOM composition caused by the prescribed fires. While this could indicate temporal resource partitioning among soil microbiota in line with the Intermediate Disturbance Theory, further work will be necessary to understand the mechanistic basis of this correlation, as we were unable to link the laccase genetic sequences to any characterized fungal species. Our observation suggests that altered accessibility of nutrients and labile C due to burning may play a key role in moderating the dynamics of SOM turnover in forest ecosystems. The effects on SOM composition and laccase genetic diversity were stronger in the plots burned in a 2-year rotation, suggesting that the 4-year burn frequency may be a more sustainable practice to ensure the long-term stability of C cycling in such ecosystems.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

This study was supported by the Scottish Executive Environment and Rural Affairs Department, ARC Linkage-Projects grant (LP0347493), ARC Linkage International Awards grant (LX0455012) and a Macaulay Development Trust Partnership Grant. We are grateful to the Queensland Department of Primary Industries for the continuing maintenance of the Forester experimental burning site and Dr Tim Blumfield and Prof. Zhihong Xu for assistance with field work. We thank Dr Brajesh Singh, Dr Andy Taylor, Dr Helaina Black, Dr David Johnson and two anonymous referees for critical comments on earlier versions of this manuscript.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
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