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Keywords:

  • climate change;
  • summer drought;
  • soil fungi;
  • organic matter decomposition;
  • phenol oxidase

Abstract

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

Natural moisture limitation during summer drought can constitute a stress for microbial communities in soil. Given globally predicted increases in drought frequency, there is an urgent need for a greater understanding of the effects of drought events on soil microbial processes. Using a long-term field-scale drought manipulation experiment at Clocaenog, Wales, UK, we analysed fungal community dynamics, using internal transcribed spacer-denaturing gradient gel electrophoresis (DGGE), over a 1-year period in the 6th year of drought manipulation. Ambient seasonality was found to be the dominant factor driving variation in fungal community dynamics. The summer drought manipulation resulted in a significant decline in the abundance of dominant fungal species, both independently of, and in interaction with, this seasonal variation. Furthermore, soil moisture was significantly correlated with the changes in fungal diversity over the drought manipulation period. While the relationship between species diversity and functional diversity remains equivocal, phenol oxidase activity was decreased by the summer drought conditions and there was a significant correlation with the decline of DGGE band richness among the most dominant fungal species during the drought season. Climatically driven events such as droughts may have significant implications for fungal community diversity and therefore, have the potential to interfere with crucial ecosystem processes, such as organic matter decomposition.


Introduction

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

Our understanding of the microbial processes governing the formation and maintenance of soil organic matter pools is far from comprehensive, and there is a pressing need for research concerning the response of soil microbial communities to environmental change. Fungi are generally considered the principal microbial decomposers in wet, acidic organic matter rich soils (Thormann, 2006), and, alongside their vital role as crucial instigators of the decomposition of recalcitrant organic material, they are of particular importance when considering the stability of soil organic carbon pools. It has been predicted that current global warming may result in a greater frequency and severity of summer droughts at higher latitudes (Houghton, 2001; IPCC, 2007). Shallow peatlands (peaty gleys and peaty iron pan soils) have been estimated to cover an area of c. 3 million hectares in the United Kingdom, representing a soil carbon store of c. 660 million t C – nearly a quarter of the total UK peatland carbon stock (Cannell et al., 1993). Tending to occupy marginal niches of low nutrient availability, cool and wet conditions, often at elevated altitude, upland heathlands dominated by Calluna vulgaris may be particularly vulnerable to environmental change (Anderson & Hetherington, 1999; Britton et al., 2003; Emmett et al., 2004; Penuelas et al., 2004).

Fungal species may vary in their tolerance of low moisture conditions (Shi et al., 2002; Gleason et al., 2004; Medeiros et al., 2006). Studies in forest ecosystems have found low forest floor fungal biomass to coincide with low moisture levels (Ohtonen & Markkola, 1991; Berg et al., 1998; Criquet et al., 2000; Osono et al., 2003; Krivtsov et al., 2006), and differences in moisture levels have been found to result in changes in forest litter and soil fungal community species richness at both temporal and spatial scales (McLean & Huhta, 2000; Trudell & Edmonds, 2004; Robertson et al., 2006). Less is known, however, about the effects of soil moisture upon fungal communities in heathland soils. While information concerning the structure and diversity of free-living soil fungal communities in upland European heathland is sparse, a number of studies have shown that the root systems of heathland ericoid plant species are colonized by a considerable diversity of ericoid mycorrhizal fungi (Perotto et al., 1996; Sharples et al., 2000; Johansson, 2001) that are also capable of a degree of saprotrophic activity (Read et al., 2004). A long-term field-scale experiment at a Calluna-dominated heathlands on an organic rich humo-ferric podsolic soil at Clocaenog, North Wales, has been set up to address how heathland ecosystems may respond to an increased frequency of summer drought (Beier et al., 2004). Previous work carried out at the Clocaenog heathland climate manipulation site has reported drought effects on both soil respiration (Llorens et al., 2002; Jensen et al., 2003; Emmett et al., 2004), and plant exudation of carbon substrates to the soil and subsequent microbial carbon fixation (Gorissen et al., 2004), suggesting that soil moisture changes may indeed be important for soil microbial communities in upland heathlands.

Extracellular phenol oxidase enzymes produced by both fungi (Burke & Cairney, 2002) and bacteria (Fenner et al., 2005) form a crucial component of the pathways involved with the breakdown of complex organic matter and the stability of soil carbon stores. While studies in deep peatlands have found drought conditions to stimulate soil extracellular phenol oxidase activity (Freeman et al., 2001; Fenner et al., 2005), phenol oxidase activity has been reported to decrease in forest litters in the typically dry conditions of the Mediterranean summer (Criquet et al., 2000). The aims of this study were, therefore, to (1) investigate the effects of manipulated summer drought on the fungal diversity and species richness of this upland heathland peaty podsoland, and (2) assess whether phenol oxidase activity during the summer drought period was correlated with any changes found in soil fungal diversity and richness.

Materials and methods

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

Site characteristics

The Clocaenog CLIMOOR experiment was situated at 490 m above sea level on a hilltop (53° 03′N, 30° 28′W) in North Wales, UK. Mean annual air temperature and rainfall at the site is 8.2 °C and 1700 mm year−1. Vegetation at the site is predominantly C. vulgaris, with low additional cover of Vaccinium myrtillus and Empetrum nigrum. The soil is classified as a humo-ferric podzol, with a peaty podzol Ah horizon (c. 6–10 cm, organic matter 89%, bulk density 0.11 g cm3, pH 3.9, base saturation 0.59 meq 100 g−1) overlaying c. 18–20 cm mineral soil (organic matter 37%, bulk density 0.86 g cm3, pH 4.0, base saturation 0.16 meq 100 g−1) on Silurian sedimentary rock. Average root biomass is 7.95 kg C m2 and average soil organic carbon is 5.43 kg C m2. Nine experimental plots (5 m × 4 m) were established at the site in 1998 to create three nonintrusive treatments (plot treatments randomly allocated): night-time warming, summer drought, and control (see Beier et al., 2004 for further details). Only the drought and control plots were used for this study. Each summer drought plot consists of a frame supporting a retractable roof made of transparent polyethylene plastic. During a dictated summer drought, rain sensors activate the motor to extend the roof over the plot when it rains and retract it again when the rain stops. (A wind sensor retracts the roofs if winds exceed 10 m s−1). Each plot is trenched to prevent surface and shallow soil water flow downslope between plots. Control plots have a frame but no roof. The drought manipulations have been running annually since 1998. The summer drought manipulation falling within the time period monitored for this study ran from 28/06/2005–21/09/2005.

Sample collection and determination of soil moisture content

Samples from the organic horizon were collected at seven dates in 2005: before the 2005 experimental summer drought period in May and June, during the drought period in July, early August and late August, and after the drought period in October and November. At each sampling date, four cores were taken from the total depth of the organic horizon at random locations within the top left-hand quarter of each plot using a 2.5 cm diameter half-moon hand-held auger. The four replicate samples were pooled to a bulk sample for each plot, mixed manually and a single 2 cm3 subsample was removed from each and frozen at −20 °C within 24 h of collection for analysis. Removal of Calluna hair roots from the samples was not attempted. Soil moisture content was evaluated from the gravimetric moisture content of three 1 cm3 subsamples dried at 105 °C to a constant weight.

DNA extraction and fungal internal transcribed spacer (ITS) PCR

DNA was extracted from 0.5 g of sample using the modified phenol–chloroform-based DNA extraction technique described by Anderson et al. (2003). This method incorporates CTAB to limit coextraction of soil-derived phenolics and humic acids. PCR amplifications of the fungal ITS rRNA regions were performed using initial amplification of the ITS region with the ITS1F (Gardes & Bruns, 1993) and ITS4 (White et al., 1990) primers. PCR reactions (50 μL) contained 2.5 μL 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 (BSA) (Roche, UK), 20 pmol of each primer and c. 50 ng of template DNA. Thermal cycling was carried out on a Dyad DNA Engine thermal cycler (MJ Research, Waltham) using the following cycling conditions: 94 °C for 5 min, then 35 cycles of 94 °C for 1 min, 55 °C for 1 min and 72 °C for 1 min, followed by a final extension step at 72 °C for 5 min. The first round PCR products were purified using the Wizard SV PCR cleanup kit (Promega, UK) according to the manufacturer's instructions. The cleaned-up first round products were then used as template for nested PCR to produce ITS1 products for denaturing gradient gel electrophoresis (DGGE) as described in Anderson et al. (2003). Nested PCR was required to obtain sufficient product for DGGE as direct amplification with the GC-clamped primers in PCR resulted in products of insufficient quantity for DGGE. The nested PCR conditions were as described above except that BSA was omitted and the cycling times were reduced from 1 min to 30 s.

Fungal ITS-DGGE analysis

DGGE was carried out using the DCode Universal Mutation Detection system (Bio-Rad, UK) using 8% polyacrylamide gels bound to the GelBond PAG film (Cambrex, UK) with a linear vertical gradient of 20–50% denaturing agents [constituting a gradient of 1.4 M urea and 8% (v/v) formamide, to 3.5 M urea and 20% (v/v) formamide, respectively]. Gels were cast using a gradient former (Fisher Scientific, UK) and a peristaltic pump at a flow rate of 4 mL min−1. Approximately 500 ng of nested PCR products or control DNA were loaded and electrophoresis was performed in 1 × TAE buffer (40 mM Tris-acetate, 1 mM EDTA) at 200 V and 60 °C for 5 h. As three separate DGGE gels were needed for the 42 samples analysed, samples from different treatments and sampling dates were randomly loaded. The gels were standardized using a standard mixture of DNA fragments (Hyperladder IV, Bioline, UK), loaded at three lane positions on each gel, and one positive control sample per gel. The positive control consisted of fungal ITS1 fragments amplified from a single organic forest soil DNA sample. DGGE gels were stained by silver staining as described in McCaig et al. (2001) and digitized by image scanning using an Epson GT-9600 scanner as uncompressed 8 bit grayscale TIFF files.

Analysis of fungal ITS-DGGE patterns

The software package gelcompar II (version 4.5, Applied Maths, Belgium) was used to compile and compare ITS-DGGE profiles. The positions of bands in the molecular weight marker were used to normalize between gels. The options for minimum profiling and minimum areas in the automatic band searching facility were adjusted to the minimum required to detect the major bands in the organic soil markers and the molecular weight markers (Artz et al., 2007). Only minor adjustments (<5% of bands detected) were performed manually where gel imperfections (e.g. spots) caused imprecise or incorrect assignments and where darkly stained regions required manual contrast adjustments to detect closely spaced bands. Bands were assigned to band classes using optimization and tolerance settings selected so that the molecular weight markers were 100% similar. The bands in the positive controls were used to check for any residual between-gel effects (Artz et al., 2007). Data for presence and absence of bands per band class were exported as a binary matrix for statistical analyses.

Phenol oxidase activity

Extracellular phenol oxidase activity was determined using 10 mM dihydroxy phenylalanine (l-DOPA) (Sigma-Aldrich Co. Ltd, Dorset) solution as a substrate, closely following the procedure of Pind et al. (1994) and modified according to Williams et al. (2000). Using a cut-off syringe, 1 cm3 of the pooled soil sample was used for preparation of a soil homogenate in 9 mL ultra-pure water, using a stomacher (Seward Colworth model 400, London, UK) (30 s, normal setting) in order to minimize cell disruption. Two aliquots of 300 μL of the homogenate were transferred to separate 1.5-mL Eppendorf tubes. Four hundred and fifty microlitres of ultra-pure water was added to each 300 μL of homogenate and the tubes cooled to field temperature (measured at each sample collection date). Following cooling, 750 μL of 10 mM l-DOPA solution or ultra-pure water (control) at field temperature was added to each sample and the samples incubated at field temperature for 9 min (mixed by vortexing at full speed at 0 and 4.5 min). Samples were centrifuged at 7 200 g (10 min) to terminate the reaction. Three analytical replicates of the resulting supernatant (300 μL) were used for absorbance measurements at 460 nm. Mean absorbance of control wells was subtracted from sample wells and phenol oxidase activity calculated using Beers Law and the molar adsorbancy coefficient for the l-DOPA product 3-dihydroindole-5,6-quinone-2-carboxylate (diqc) (3.7 × 104, Mason, 1948) and expressed as nmol dicq of produced min–1 g–1 sample (dry weight).

Statistical analyses

Soil moisture and phenol oxidase data were analysed for statistical significance between control and summer drought treatment during ‘predrought’, ‘during drought’ and ‘postdrought’ sampling periods by one-way anova. DGGE bands are indicative of the most abundant species in the community only and individual bands may represent more than one species and individual species may be represented by more than one band (Loisel et al., 2006). The number of bands can, therefore, only be interpreted as an approximation of the species richness of the dominant members of a community and in order to highlight this and avoid any misinterpretation of the DGGE results the total number of ITS-DGGE bands for each sample has been termed band richness. Treatment effects on band richness were tested using anova. As the DGGE data were of a binary nature and hence did not satisfy models assuming multivariate normality, we used a permutated nonparametric test (permanova, Anderson, 2001) to test for the effects of treatments, time and their interaction. Within-group differences at each hierarchical level were tested with post hoc-permutated tests within permanova. Analysis of relationships between soil moisture content or phenol oxidase activity and band richness were performed using linear regression. The BEST procedure in Primer6 (Primer-E Ltd, Plymouth, UK) was used to test for correlation between soil moisture content or phenol oxidase activity with fungal community composition. This procedure selects environmental variables that ‘best explain’ community patterns, by maximizing Spearman's rank correlations between their respective resemblance matrices. Significance is tested using random permutation (n=99) of sample names. The Bray–Curtis dissimilarities resulting from permanova were used in nonmetric multidimensional scaling (NMDS) within Genstat (8th edn, VSN International) where the number of dimensions was chosen on the basis of minimum stress. The data was considered both overall for the entire sampling period and for the ‘predrought’ (May and June), ‘during drought’ (July, early- and late-August) and ‘postdrought’ (October and November) sampling periods separately.

Results

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

Soil moisture

Moisture levels in the organic horizon of the drought manipulation plots were found to be already around 10% lower than those in the control plots during the predrought period (Fig. 1a, difference not significant). Moisture levels in the drought plots were found to be significantly lower than the controls during the drought period (P=0.01), with soil moisture in the drought plots decreasing progressively throughout the summer drought reaching almost 50% lower soil moisture than the controls by the late August sampling date (Fig. 1a). Following removal of the drought roofs (postdrought), moisture levels in the drought plots recovered to a similar moisture content to that of the controls by the October sampling date (Fig. 1a, difference not significant).

image

Figure 1.  Temporal variation in (a) gravimetric moisture content and (b) soil phenol oxidase activity, in the organic soil horizon in the control (♦) and summer droughted (▵) plots. Data shown are treatment means±SEs of n=3. The section between the dotted lines indicates the period during which the summer drought manipulation was applied.

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Soil phenol oxidase activity

No significant difference in phenol oxidase activity in the organic horizon was found between the controls and the summer drought treatment plots before the drought period (Fig. 1b). The summer drought treatment then significantly reduced soil phenol oxidase activity in the drought plots (P<0.001 drought period), (Fig. 1b). No significant difference in soil phenol oxidase activity was found between the controls and the summer drought treatment plots for the postdrought period (Fig. 1b).

Drought effects on fungal ITS-DGGE band richness

Figure 2 illustrates both the temporal variation over the entire sampling period and divergence of band richness between the drought and control soil during the summer drought period. While a progressive loss of ITS-DGGE bands occurred in both the control and drought treatments over the summer drought period (Fig. 3), a much greater number of fungal ITS bands were lost from the drought than the control soil. Sampling date was found to have a significant effect on band richness for the sampling period overall and for the predrought and during drought sampling periods considered separately, but not for the postdrought period (Table 1). The drought treatment showed a strong trend (P=0.061) on band richness for the sampling period overall; this, however, explained just 2.1% of the overall variance in band richness, clearly illustrating the overriding effect of seasonality in determining fungal band richness. The anova results for pre-, during-, and postdrought periods separately, however, reveal that despite the strength of this temporal factor, there was a significant effect of the drought treatment on band richness during the drought period that explained 15% of the variance in band richness (Table 1). The interaction between the temporal factor and the drought treatment was significant only during the drought period (Table 1).

image

Figure 2.  Example of DGGE gel showing fungal community composition in some of the soil samples obtained from the CLIMOOR drought-manipulation experiment. Lanes are labelled according to treatment above the gel image (M, molecular weight marker; +, positive control sample; C, control; D, drought) and according to sampling point below the gel image (−, not applicable for marker lanes; M, May; Jn, June; Jl, July; EA, early August; LA, late August; O, October; N, November).

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image

Figure 3.  Temporal variation in DGGE band richness, in the organic soil horizon in the control (♦) and summer droughted (▵) plots. Data shown are treatment means±SEs of n=3. The section between the dotted lines indicates the period during which the summer drought manipulation was applied.

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Table 1.   Statistical analysis of the effects of plot, sampling date, treatment and treatment × date interaction on peat fungal band richness and fungal community composition over the entire sampling period, and within the pre-, during, and postinduced drought periods
FactorFungal band richness*Fungal community composition
OverallPredroughtDuring droughtPostdroughtOverallPredroughtDuring droughtPostdrought
  • *

    Effects of experimental factors were determined by two-way anova (percent variance explained was calculated from sum of squares).

  • Effects of experimental factors on fungal community composition were determined by permanova (percent variance explained was calculated from sum of squares as SS per factor/ total SS). NA, not applicable as factor not significant overall.

Plot4.0%NANANA10.9%14%9.0%42.1%
(P=0.908)   (P=0.001)(P=0.060)(P=0.028)(P=0.001)
Treatment2.1%0.6%15.3%0.04%3.3%7.7%13.6%15.7%
(P=0.061)(P=0.631)(P=0.002)(P=0.954)(P=0.26)(P=0.594)(P=0.144)(P=0.117)
Date71.4%76%65.3%9.1%32.6%26%25.2%7.8%
(P<0.001)(P<0.001)(P<0.001)(P=0.397)(P=0.001)(P=0.005)(P=0.003)(P=0.206)
Treatment × date10.6%5.1%8.3%0.04%15.8%9.8%13.0%4.1%
(P=0.018)(P=0.173)(P=0.036)(P=0.954)(P=0.001)(P=0.162)(P=0.062)(P=0.419)

Drought effects on ITS-DGGE fungal community composition

Figure 4 shows the distinct seasonal variation in fungal community composition in both the control (Fig. 4a) and drought (Fig. 4b) treatments over the entire period of measurement. Band patterns of the positive controls were closely matched among DGGE gels on NMDS axes 1 and 2 (data not shown) and indicated that there was no residual between-gel variability. The drought treatment appears to emphasize the temporal dynamics by increasing the distance between samples taken within the drought period along both the first and second NMDS axis. The fungal community composition does, however, appear to follow roughly similar trajectories in the control and drought plots, with the postdrought composition returning towards the start position in multivariate space. permanova revealed a significant effect of sampling plot on fungal community composition for all 42 samples overall and during drought and postdrought periods separately (Table 1). Plot effects (i.e. differences in soil fungal diversity caused by underlying gradients, e.g. differences in vegetation, soil drainage) were, therefore, taken into account using plot number as a covariate for the statistical analyses of the overall, during drought and postdrought data sets. Sampling date was found to have a significant effect on fungal community composition for the sampling period overall, and for the predrought and during drought sampling periods considered separately, but not for the postdrought period (Table 1). The drought treatment was found to have no significant effect on fungal community composition overall or for the pre- during- and postdrought sampling periods considered separately (Table 1). Sampling date and treatment were, however, found to have a significant interactive effect overall and there was a strong trend for an interactive effect during the drought period (Table 1).

image

Figure 4.  Nonmetric multidimensional scaling (stress=0.198) of the temporal changes in fungal community composition in the control (a) and summer-droughted (b) heathlands. Data shown are averages±SEM (n=3) for each sampling point (May, filled circles; June, circles; July, filled squares; Early August, squares; Late August, crossed squares; October, filled triangles; November, triangles). Stress was 0.197, and graphs were split for clarity only. Arrows are shown to illustrate the direction of the temporal changes only. Letters (S, start and F, finish) have been added below the datapoints for May and November, respectively, to aid visual clarity of the trajectory.

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Relationships between fungal community composition/band richness and soil moisture

Soil moisture levels in the organic horizon soil and band richness appeared to show a positive linear relationship during the drought period (Fig. 5a). Linear regression, however, found no significant relationship between soil moisture and band richness for drought period, the pre- and postdrought periods or overall (Table 2). We observed a significant positive Spearman's rank correlation between soil moisture levels in the organic horizon and the resemblance matrix of the fungal community composition for the sampling period overall, and analysis of the pre-, during and postdrought periods separately reveals that this overall significance was driven by a significant positive Spearman's rank correlation during the drought period only (Table 2).

image

Figure 5.  Linear regression of (a) gravimetric moisture content and DGGE band richness and (b) DGGE band richness and phenol oxidase activity in the organic soil horizon in the control (♦) and summer droughted (▵) plots during the summer drought period.

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Table 2.   Relationships of fungal band richness and fungal community composition with phenol oxidase activity and soil gravimetric moisture content over the entire sampling period, and within the pre-, during, and postinduced drought periods
FactorFungal band richness*Fungal community composition
OverallPredroughtDuring droughtPostdroughtOverallPredroughtDuring droughtPostdrought
  • *

    Correlations between measured soil moisture or phenol oxidase activity with band richness were determined using linear regression (*residual variance exceeds variance of response variate).

  • Spearman's rank correlations between the resemblance matrix of the fungal community composition with phenol oxidase activity or soil moisture were determined using the BEST algorithm in Primer6.

Moisture content*7.9%10.2%18.3%0.202−0.1080.3090.051
(P=0.427)(P=0.193)(P=0.106)(P=0.093)(P=0.003)(P=0.78)(P=0.001)(P=0.32)
Phenol oxidase*2.7%24.2%*0.3190.3290.3340.015
Activity(P=0.914)(P=0.28)(P=0.022)(P=0.773)(P=0.001)(P=0.04)(P=0.002)(P=0.42)

Relationship between fungal community composition/band richness and soil phenol oxidase activity

Band richness and soil phenol oxidase activity were found to show a significant positive linear relationship (24.2% of variance explained, P=0.022 in linear regression) during the drought period (Fig. 5b; Table 2). This was confined to the drought period only, with no significant linear relationships between band richness and soil phenol oxidase activity found outside of the drought period (Table 2). A significant positive Spearman's rank correlation between the resemblance matrix of the fungal community composition and soil phenol oxidase activity in the organic horizon was observed for the sampling period overall, and for the pre- and during-drought periods (Table 2).

Discussion

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

The drought roof treatment was highly effective in reducing soil moisture levels in the drought plots during the summer drought period, while the organic horizon soil rewetted within 3 weeks after the drought manipulation period to control levels. Seasonality, rather than the drought treatment, was found to be the dominant factor driving changes in fungal community dynamics over the sampling period. The progressive decline of species richness among the most dominant fungal species in the control plots over the summer manipulated drought period was not partnered with any evidence for soil moisture loss in the control plots, suggesting that the decline of band richness with drought was accentuating a trend for natural summer decline of richness. While it is feasible that DNA extraction efficiency is not equally as effective at extracting DNA from very dry soil, other studies have shown that soil drying does not hamper the extraction of microbial DNA (Rosado et al., 1996; Klammer et al., 2005). Similarly, although the limitations of DGGE analysis have been recently highlighted (Loisel et al., 2006), the significant loss of DGGE band richness from the drought treatment plots during the drought period, when band richness was below the saturation point of the analytical technique, suggests that the drought induced changes in fungal band richness and community composition may have largely resulted from reductions in the species richness of dominant fungal species. That fungal biomass suffered over the drought period is in keeping with previous findings that soil microbial carbon fixation at the Clocaenog site is reduced by the summer drought treatment (Gorissen et al., 2004). The recovery of species richness following the summer drought may have been facilitated by small amounts of hyphae that persisted through the drought, or from desiccation-resistant spores (Evdokimova & Mozgova, 2003; Gleason et al., 2004). Seasonality has been observed in other fungal communities (Mulder & de Zwart, 2003; Schadt et al., 2003), and it may also be that other factors such as temperature, vegetation–soil interactions and substrate availability act to drive natural summer declines among the most dominant fungal species at the site. Previous work at the Clocaenog site found that plant photosynthetic rates (Llorens et al., 2004) and carbon flow from plant roots to the soil (Gorissen et al., 2004) are reduced during the summer drought treatment, suggesting that reductions in plant-derived labile carbon may have contributed to a decline in fungal species over the summer drought period through indirect reduction in fungal growth and activity due to substrate limitation.

The ecological interpretation of the observed relationship between fungal band richness and community composition with soil moisture would depend on the trophic status of the fungi affected by the moisture limitations. Depending on the severity of the drought, some recent reports open up the possibility that fungal species in close association with roots may be more protected from the effects of drought than other soil fungi due to their ability to attain water from the host via night-time hydraulic lift (e.g. Querejeta et al., 2007). Indeed, Staddon et al. (2003) showed an increase in the relative proportion of colonized root length by arbuscular mycorrhizae in a grassland system subjected to drought. Although those studies were performed with nonericaceous plants where root access to deeper water resources is possible, ericaceous hosts may be able to protect their mycorrhizal symbionts in a similar manner. The root system of C. vulgaris, for example, although very shallow, forms a dense network of superficial roots that have the ability to grow towards the soil surface and are hence able to intercept incident moisture very effectively (Gimingham, 1972). As the drought roofs did not exclude rainfall in windy conditions this could result in a possible competitive advantage to the survival and/or fitness of ericoid mycorrhizal fungi during the summer drought.

The ability of a soil fungal community to degrade recalcitrant substrates has been shown to be reduced by loss of species richness (Setala & McLean, 2004), and it may be that this has contributed to the lowering of litter decomposition rates previously reported in the Clocaenog drought plots (Emmett et al., 2004). Consistent with this, the summer drought treatment was found to result in significant reductions in soil extracellular phenol oxidase activity. Soil phenol oxidase activity was significantly correlated with changes in fungal community composition and showed a significant positive linear relationship with band richness during the drought period. This suggests that the decline in phenol oxidase activity may have been a result of declining populations of phenol oxidase producing fungal species, perhaps in conjunction with lower enzyme production. Other studies in forest ecosystems have also found fungal species richness and the diversity of laccase-encoding genes present within the bulk soil fungal community to be positively correlated with soil phenol oxidase activity (Luis et al., 2005; Blackwood et al., 2007). Given that individual fungal species may possess a number of different laccase-encoding genes, the diversity of which can differ markedly among fungal species (Luis et al., 2005), the decline of certain phenol oxidase producing species may have significant effects on overall soil phenol oxidase activity. At the time of this study, the status of the enzymic capabilities of bulk soil saprotrophic fungi in heathlands appears not to have been investigated; however, phenol oxidase activity has been extensively reported from saprotrophic fungal isolates from related ecosystem types such as peatlands (Thormann et al., 2002; Artz et al., 2007). It is interesting to note that the correlation between fungal band richness and community composition with phenol oxidase activity was significant during the summer drought period but not outside the treatment period. It is, therefore, possible that this correlation is driven by those fungi that persist during the summer season and were additively affected by the drought treatment. Although the relative fate of soil saprotrophic vs. mycorrhizal fungi during drought events in heathlands is as yet unstudied, some other work points in the direction of an involvement of the mycorrhizal partners of heathland plants. Ericoid mycorrhizal fungi have been reported to express phenol oxidase (including laccase and catechol oxidase) activity (Bending & Read, 1997; Cairney & Burke, 1998; Thormann et al., 2002) as part of their arsenal of enzymic capacities that enables utilization of organic nitrogen sources to supply the host with nutrients (Read et al., 2004). As yet, there is a paucity of studies that have addressed whether there are seasonal variations in the requirement for phenol oxidase expression by ericoid mycorrhizae during plant growth in ericaceous species. In ectomycorrhizal systems, however, Courty et al. (2007) were able to show a relationship between laccase activity (among other enzymes) of root tips infected with Lactarius quietus and times of increased requirement of carbon mobilization of the host, such as bud break and leaf expansion. They speculated that this may imply that this ectomycorrhizal species could switch to saprotrophic growth during periods of high demand for carbon by the host plant. In an analogy, in this system, the reduced photosynthetic capacity during drought events and associated lower allocation of carbon to soil microbial biomass (Gorissen et al., 2004; Llorens et al., 2004) may induce saprotrophic growth in the mycorrhizal fungi associated with heathland plants. A more comprehensive seasonal survey of phenol oxidase activity at Clocaenog in the same year as this study, however, did not provide any evidence of a seasonal optimum (Toberman et al., 2008), although the semi-evergreen nature of heathland vegetation may place less of a burden on carbon mobilization than in deciduous hosts.

The correlation found between fungal band richness/community composition and soil phenol oxidase activity at the site also does not necessarily imply a direct causal relationship. Other soil physicochemical variables that may change with drought include pH, oxygen availability and temperature stability (Anderson, 1991). Drought induced changes in leaching may also change the amounts and solubility of substrates available to the fungal community (Freeman et al., 1996; Kang & Freeman, 1997; Sowerby et al., 2005). Other factors may include direct effects of low soil moisture on the activity, solubility and mobility of phenol oxidases and we previously showed, using a soil core drying experiment (excluding plants but including severed Calluna roots, thus encompassing their mycorrhizal fungi, although in a state where they no longer receive carbon from their hosts), that there appears to be a soil moisture optimum for phenol oxidase activity in this soil (Toberman et al., 2008).

In summary, summer drought negatively affected both fungal band richness and community composition. The observed relationships between simultaneously decreased soil moisture and phenol oxidase with the changes in fungal community composition and band richness during experimental drought could be driven by a number of factors such as decreased allocation of photosynthetic carbon to the soil during drought which could directly affect fungal growth and activity, but also indirectly by potentially altering carbon dynamics between mycorrhizal fungi and their hosts. Similarly, however, the potential direct effects of drought include decreased mobility of both carbon substrates and enzymes due to moisture limitation. The observation that the relationship between phenol oxidase activity and fungal band richness or community structure is only significant during the drought period suggests differential effect of drought on the specific fungal community that produces phenol oxidases. Future studies should, therefore, aim to determine the key fungal phenol oxidase producing species affected by drought and secondly, how drought processes modulate enzyme expression and activity from such fungi. Crucial ecosystem implications of climatically driven changes in fungal community composition and associated phenol oxidase activity may include changes in enzyme inhibiting soil phenolics (Freeman et al., 2004), and hence there is a possibility that, in the longer-term, the storage : decay balance of recalcitrant organic matter in such organic soils is affected.

Acknowledgements

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

H.T. was supported by a PhD studentship jointly funded by the University of Wales, Bangor (Llewelyn and Mary Williams Scholarship), CEH Bangor and the Macaulay Development Trust. The Clocaenog experiment was supported by the EU framework program 6 (Eurolimpacs) and framework 5 (Vulcan) projects. We are grateful for support from the Royal Society (C.F.) and The Leverhulme Trust (C.F. and N.F.) and the Scottish Government (R.A.).

References

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