Soil environmental factors rather than denitrification gene abundance control N2O fluxes in a wet sclerophyll forest with different burning frequency

Soil Biology & Biochemistry 57 (2013) 292e300

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Soil environmental factors rather than denitrification gene abundance control N2O fluxes in a wet sclerophyll forest with different burning frequency Xian Liu a, C.R. Chen a, *, W.J. Wang b, J.M. Hughes c, Tom Lewis d, E.Q. Hou a, Jupei Shen a a

Environmental Futures Centre and Griffith School of Environment, Griffith University, Nathan, QLD 4111, Australia Soil Processes, Department of Science, Information Technology, Innovation and the Arts, 41 Boggo Road, Dutton Park, QLD 4102, Australia c Australian River Institute and Griffith School of Environment, Griffith University, Nathan, QLD 4111, Australia d Horticulture and Forestry Science Agri-Science Queensland, DAFF (Department of Agriculture, Fishery and Forestry), 4558, Australia b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 24 July 2012 Received in revised form 23 September 2012 Accepted 6 October 2012 Available online 2 November 2012

Production of nitrous oxide (N2O) by anaerobic denitrification is one of the most important processes in the global nitrogen (N) cycle and has attracted recent attention due to its significant impacts on climatic change. Fire is a key driver of many ecosystem processes, however, how fire drives the shift in microbial community and thus alters nutrient cycling is still unclear. In this study, a 35-year-old repeated prescribed burning trial, with three treatments (no burning, 2 yearly burning and 4 yearly burning), was used to explore how the long-term repeated prescribed burning affects N2O flux, key soil properties (inorganic N, dissolved organic carbon (DOC) and N, pH, electrical conductivity (EC), moisture), denitrification gene abundance and their interactions. Soil samples were collected in January and April 2011. Quantitative real-time PCR was employed to quantify the gene copy number of target genes, including narG, nirK, nirS and nosZ. In situ N2O fluxes ranged from 0 to 8.8 g N2OeN ha
1 h
1 with an average of 1.47 g N2OeN ha
1 h
1. More frequent fire (2 yearly burning) significantly reduced soil N2O fluxes, availability of C and N substrates and moisture, but increased soil pH and EC compared with no burning and 4 yearly burning treatments. Fire treatments did not significantly affect the abundance of most denitrification genes. There were no significant differences in most parameters measured between the 4 yearly burning and no burning treatments, indicating microbial community function is not affected by less frequent (4 year interval) burning. Variation in the N2O fluxes among the treatments can largely be explained by soil substrate (NO3
, DOC and total soluble nitrogen (TSN)) availability and soil environmental factors (pH, EC, and moisture), while the abundance of most denitrification genes were not related to the N2O fluxes. It is concluded that soil environmental factors rather than denitrification gene abundance control N2O fluxes in this wet sclerophyll forest in response to long-term repeated fires. Ó 2012 Elsevier Ltd. All rights reserved.

Keywords: Prescribed burning In situ N2O flux Soil environmental factors Denitrification genes Real-time PCR

1. Introduction Production of nitrous oxide (N2O) by anaerobic denitrification is one of the most important processes in the global nitrogen (N) cycle and has attracted recent attention due to its significant impacts on climatic change (IPCC, 2007). This nitrogen (N) transformation process is of global interest because it can result in significant N losses from ecosystems, and the gaseous product N2O depletes stratospheric ozone and contributes to global warming (Ravishankara et al., 2009). The N2O is a potent greenhouse gas which has a global warming potential about 298 times greater than that of carbon (C) dioxide (Nakicenovic and Swart, 2000).

* Corresponding author. Tel.: þ61 7 3735 7494; fax: þ61 7 3735 7773. E-mail address: c.chen@griffith.edu.au (C.R. Chen). 0038-0717/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.soilbio.2012.10.009

There is a large diversity of bacteria, archaea, and fungi involved in denitrification, and their abundance and composition are likely to be affected by changes in soil type and environmental factors. It is well known that soil organic C and NO3
availability  (Klemedtsson et al., 2005), pH (SImek and Cooper, 2002), O2 and water content (Bateman and Baggs, 2005) can influence the denitrification rates. These factors are likely to shape the variable structure and function of the microbial communities, whose composition mirrors and integrates the long-term effects of environmental change and resource availability (Wallenstein et al., 2006). On the other hand, the denitrifying community directly mediates the reducing processes of NO3
to produce N2O and N2. However, the processes/mechanisms governing the interaction of the denitrifying community, resource availability and environmental factors are largely unknown. Despite a large body of work carried out to explore the relationship between environmental

X. Liu et al. / Soil Biology & Biochemistry 57 (2013) 292e300

factors (soil pH, moisture, temperature, N substrate availability etc.) and denitrification (Weier et al., 1993; Bossio et al., 1998), fewer studies have attempted to gain a better understanding of the biological aspects of denitrification (Mergel et al., 2001; Rösch et al., 2002; Taroncher-Oldenburg et al., 2003; Wakelin et al., 2007). Denitrification consists of four enzymatically catalyzed reductive steps: NO3
reduction ðNO3
/NO2
Þ, NO2
reduction ðNO2
/NOÞ, NO reduction (NO/N2O) and N2O reduction (N2O/N2) (Philippot, 2002; Ellen et al., 2006). These processes involve multiple genes encoding four metalloenzymes, including dissimilatory NO3
reductase, NO2
reductase, NO reductase, and N2O reductase (Canfield et al., 2010). The dissimilatory NO3
reductase comprises two homologous enzymes- membrane-bound (Nar) and periplasmic-bound (Nap) NO3
reductase- which are encoded by the narGHJI operon and the napABC operon, respectively (Philippot and Højberg, 1999). Because NO3
reductase also functions as a NO3
respirer or reducer in the process known as dissimilatory reduction of NO3
to ammonia (NH4 þ ) [DRNA], narG and napA genes do not necessarily represent the denitrifying bacteria (Philippot, 2002; Chèneby et al., 2003; Wallenstein et al., 2006). The reduction of NO2
to NO distinguishes denitrifiers from other NO3
-respiring bacteria. This reaction is catalyzed by two different types of NO2
reductases (Nir), either a cytochrome cd1 encoded by nirS or a Cu-containing enzyme encoded by nirK. These genes are the first and most widely used molecular markers in the studies of denitrifying communities (Braker et al., 1998; Henry et al., 2004). In the next step, the NO reductase encoded by the norB gene is responsible for conversion of NO to greenhouse gas N2O. The last step of denitrification, from N2O to N2, is catalyzed by N2O reductase and is encoded by the nosZ gene. The N2O is mainly produced through denitrification under anaerobic conditions while autotrophic and heterotrophic nitrification can also contribute to the N2O flux (e.g. Inubushi et al., 1996; Canfield et al., 2010). An increasing number of studies have reported the distinct and significant role of nitrifier denitrification in the N2O production from soil (Wrage et al., 2001; Venterea, 2007; Kool et al., 2011). However, it still remains elusive to what extent changes in N2O production are explained by changes in the abundance of denitrifiers/nitrifiers, along with soil environmental factors. Fire is one of the key drivers of the diversity and function of terrestrial ecosystems (Orians and Milewski, 2007) and one of the most important environmental changes that have been extensively studied (Guinto et al., 1999; Chen and Xu, 2010; Williams et al., 2011; Burton et al., 2011). Prescribed fire is widely used as a forest management tool to reduce fuel loads (Guinto et al., 1999; Pyke et al., 2010; Ryan et al., 2010). In general, fires immediately lead to the loss of C and N as gases and particulates into the atmosphere from the ecosystem, and to transformation of phosphorus (P) from organic to inorganic forms (Carter and Foster, 2004; González-pérez et al., 2004; Galang et al., 2010). Fire can also cause an immediate increase in N availability ðNO3

N and=or NH4 þ
NÞ (Wan et al., 2001; Carter and Foster, 2004). For example, Deluca et al. (2002) reported that N mineralization and nitrification rates decreased with time after last fire due to the increasing N immobilization with successional C loading in a fire chronosequence study. Fires can lead to a drastic reduction in soil microbial biomass in the short term and cause a shift in bacterial and fungal communities in forest soils (Prieto-Fernández et al., 1998; Pietikäinen et al., 2000; Bastias et al., 2006). Nevertheless, the understanding of the impact of long-term repeated prescribed burning on the interactive links of denitrifying communities, soil environmental factors and N2O flux are still incomplete. The objective of this study was to examine the effect of longterm repeated prescribed burning on soil N availability, in situ

293

N2O flux, and denitrification gene abundance in a wet sclerophyll forest. It was hypothesized: a) that long-term repeated prescribed burning would reduce soil N2O flux by decreasing the abundance of denitrification genes and the N substrate availability; and b) a combination of soil N substrate availability, soil environmental factors (e.g. pH, moisture etc) and abundance of denitrification genes govern the N2O flux from soils. 2. Material and methods 2.1. Experimental site The research site is located in Peachester State Forest, southeast Queensland (26 500 S, 152 530 E) and was described in detail by Guinto et al. (1999). In brief, it is a native wet sclerophyll forest dominated by blackbutt (Eucalyptus pilularis Smith). Other canopy tree species include red bloodwood (Corymbia intermedia R. Baker), tallowwood (E. microcorys F. Muell.), red mahogany (E. resinifera Smith), turpentine (Syncarpia glomufera (Smith) Niedenzu) and brush box (Lophostemon confertus (R. Br.) P.G. Wilson & Waterhouse). The understory vegetation is variable and species-rich, in places dominated by grasses (e.g. Imperata cyclindrica (L.) Rauschel, Digitaria ciliaris (Retz.) Koeler), ferns (Pteridium esculentum (G. Forst.) Cockayne, Blechnum cartilagineum Sw), or shrubs (e.g. Dodonaea triquetra Andr., Hibiscus heterophyllus Vent., Hovea acutifolia Cunn. ex G. Don) (Lewis et al., in press). Average annual rainfall in this area is 1711 mm. Topography is undulating-to-rolling (2e16% slopes). The soil is deep and sandy having no perceptible increase in clay content to a depth of 60 cm. The soil is classified as yellow to red Kandosols (Isbell, 1996) (Alfisols, USDA classification). The prescribed burning experiment was established in 1972 and consists of three treatments: (1)2 yearly burning (on average), (2) 4 yearly burning (on average) and (3) no burning. Prescribed fires were carried out in winter and are generally of low intensity (<2500 kW m
1). There have been no wildfires at the site since 1969, no logging since the 1950s, and no fertilizer application or other silvicultural practices have been applied since the establishment of the burning experiment. There were four replicates for each treatment, with a total of 12 plots (30  27 m) randomly arranged across the experimental site. The latest burning for each treatment before sampling was conducted in 2007 (for 4 yearly burning plots) and 2009 (for 2 yearly burning plots). Therefore, this study sought to examine the long-term impacts of repetitive fire treatment on soil chemical and biological properties. The atmospheric temperature in the experimental site ranged from 17.5  C to 29  C in January and 15.5  Ce26.5  C in April, while the 7 day cumulative rainfall prior to the sampling was 39.3 mm (January) and 48.9 mm (April). 2.2. Soil sampling and analyses Soil samples were taken in January and April 2011. For each sampling, approximately 10 cores of surface soils (0
10 cm) were collected using a corer (7 cm in diameter) from each treatment plot and combined to produce one composite sample. Soils were sieved to 4 mm immediately after sampling and stored at field moisture content in plastic bags at 4  C prior to analysis (denitrification capacity was determined within 3 days). Subsamples for molecular analyses were sieved to 2 mm and stored at
80  C. Soil pH and electrical conductance (EC) were measured with a pH/EC meter using a soil-to-water ratio of 1:5. Soil moisture was determined gravimetrically by drying the soil at 105  C for 24 h, and all results were expressed on an oven-dry basis. Microbial biomass C (MBC) and N (MBN) was determined by the chloroform fumigation extraction method as described by Vance et al. (1987) and

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X. Liu et al. / Soil Biology & Biochemistry 57 (2013) 292e300

Jenkinson (1988). Soil mineral N (NH4 þ
N, NO3

N) was extracted using 2 M KCl and measured by Westco Smart Chem Discrete Wet Chemistry Analyzer (Westco Scientific Instruments, USA). Total C (TC) and N (TN) were measured by an isotope ratio mass spectrometer with a Eurovector Elemental Analyser (Isoprime-EuroEA 3000, Milan, Italy). Dissolved organic carbon (DOC) and total soluble nitrogen (TSN) were extracted by deionized water at 70  C for 16 h using a soil-to-water ratio of 10g/50 ml, then were measured using a TOC-VCPH/CPN analyser fitted with a TN unit (Shimadzu Scientific Instruments, Columbia, USA).

were carried out in an Effendorf MastercyclerÒ (Effendorf, Hamburg, Germany) ep realplex real-time PCR system in triplicate. The 20 mL PCR mixture contained 10 mL of SYBR green PCR Master Mix (Takara SYBRÒ Premix Ex TaqÔ (Perfect Real Time)), 0.4 mL of each primer (10 mM) and 12.5 ng of DNA. Melting curves and agarose gel running of PCR products at the end of each quantitative real-time PCR were used to check amplification specificity. Notemplate controls gave null or negligible values. The presence of PCR inhibitors in DNA extracted from soil was estimated by a 1:10 soil DNA dilution; no inhibition was detected.

2.3. N2O emission measurement

2.6. Statistical analyses

The in situ N2O gas fluxes were determined using non-vented static PVC chambers consisting of a permanent base (bottom part) and a removable lid with a rubber septum for gas sampling. Both the base and lid are 24 cm in diameter, with a height of 15 cm for the base and 20 cm for the lid. The base was inserted 10 cm into the soil two months before the commencement of gas sampling to minimize the effects of disturbance. Therefore, the chamber has a basal area of 452 cm2 and average head space of 11.31 dm3. The lid was secured tightly to the base by a gas-tight rubber seal (8 cm wide) and gas samples were taken after a 1hr accumulation period. Gas samples (26 ml) were collected using gas-tight syringes at around 9e10 am on 25/1/11, 09/03/2011, 12/04/2011, 16/05/2011, 15/06/2011 and 12/07/2011 and stored in evacuated glass tubes. The gas samples were analyzed for N2O concentration using a Varian CP-3800 gas chromatograph (Varian Inc., Middelburgh, Netherlands) as described by Wang et al. (2011).

Two-way analysis of variance (ANOVA) with repeated measures was used to determine if there was a significant difference in the interaction of sampling time and fire treatment. The copy numbers of all functional genes were log-transformed, and the normality of all data was checked and met before ANOVA. One-way ANOVA was used to evaluate the effect of different prescribed fire regimes at each sampling time since there was no significant interaction between the treatment (fire) effects and the sampling time for all parameters measured during two-way ANOVA. If the difference between the treatments was significant, the Tukey HSD test was carried out to separate the mean at 5% level among the fire treatments. Pearson correlation analysis was conducted to determine the relationships among N2O flux, abundance of N-associated functional genes, and soil parameters using all data from the 12 plots. All above statistical analyses were performed using SPSS 16. Canonical Correspondence Analysis (CCA) was carried out to assess the effects of soil environmental factors on the abundance of N-associated gene species using Canoco for Window 4.5. In addition, data on all soil properties and functional genes were subjected to principal component analysis (PCA) to distinguish the effects of fire treatments on soil using Statistica Version 6.1 (Statsoft, Inc.).

2.4. Determination of denitrification capacity (DNC) A modified method of Tiedje (1994) and Drury et al. (1998) was used for DNC analysis. Briefly, soil slurries were made by mixing 50 g field moist soil samples with 50 ml media solution, which contained 1 mM KNO3, in a 250 ml glass flask. The flasks were sealed with gas-tight lids and rubber stoppers, and flushed with N2 gas to create an anaerobic condition. The slurries were then incubated with acetylene (10%, vol/vol) at 125 rpm and 25  C in a shaking incubator. After 30 and 90 min, gas samples were taken with a gas-tight syringe and N2O concentration was quantified using a gas chromatography as described above. After correction for the dissolved N2O in water using the Bensen relationship (Tiedje, 1994), the DNC was calculated from the N2OeN production during the 30e90 min. 2.5. DNA extraction and quantification of the gene abundance DNA was extracted from 0.3 g of the soil samples using the MoBio PowersoilÔ DNA Isolation Kit (MO BIO Laboratories, Carlsbad, CA, USA) according to the manufacturer’s instruction, with the final elution step in deionised water instead of TE buffer. DNA quantity was determined using a Nanodrop spectrophometer (Thermoscientific). Extracted DNA was stored at
80  C. The PCR fragments of all functional genes were amplified using primers and thermal conditions described in the supplementary information (Table S1) and the purified PCR products were cloned into the TOPO TA cloning vector (invitrogen). Plasmids as standard used for quantification analysis were extracted from the positive clones by using a Qiagen Miniprep kit (Qiagen, Germantown, MD, USA). Standard curves were obtained using 10-fold serial dilutions of plasmid DNA containing cloned nifH, narG, nosZ, nirK, nirS, amoA and 16S rRNA genes and spanning 7 orders of magnitude. Each standard curve was run in duplicate. The total bacterial community was quantified using 16S rRNA as a molecular marker. Reactions

3. Results 3.1. Soil basic chemical and biological parameters Two-way ANOVA showed there were no significant interactions between the sampling time and the treatment (Table 1). Therefore, one-way ANOVA was used to test if fire had significant impacts on soil properties at each of the sampling times. The prescribed burning treatments had significant impacts on most soil properties measured at both the January and April sampling times (Table 2). Both 2 yearly and 4 yearly burning treatments had significantly higher soil pH compared with the no burning treatment in both January and April (Table 2). On the other hand, the 2 yearly burning treatment had significantly lower soil EC compared with the no burning treatment at both sampling times, while there was no significant difference between the 2 yearly burning and the 4 yearly burning and between the 4 yearly burning and the no burning treatments (Table 2). The 2 yearly burning had significantly lower soil TC and TN contents compared with the 4 yearly burning and the no burning treatments at both sampling times (Table 2), while there was no significant difference between the no burning and the 4 yearly burning treatments. Prescribed burning did not affect concentrations of NO4

N at both sampling times, while concentrations of NO3

N were significantly lower in the 2 yearly burning treatment compared with the no burning treatment (Table 2). Concentrations of DOC and TSN were significantly lower in the 2 yearly burning treatment than in both the 4 yearly burning and no burning treatments in January. In April, concentrations of DOC and TSN were also significantly lower in the 2 yearly burning

X. Liu et al. / Soil Biology & Biochemistry 57 (2013) 292e300

295

Table 1 P values from Two-Way ANOVA (with repeated measure) of effects of prescribed burning (B), sampling time (S) and their interaction (B  S) on soil chemical and biochemical properties at the Peachester site, Queensland, Australia. Sourcea B S BS

df 2 1 2

pHb 0.001 0.19 0.45

Moisture ***

0.001 0.67 0.97

***

EC ***

0.001 0.01** 0.62

NO3

N

NH4 þ
N

TC

TN

DOC

TSN

MBC

MBN

DNC

N2O

0.07 0.63 0.20

0.07 0.87 0.17

0.75 0.001*** 0.94

0.001*** 0.39 0.92

0.001*** 0.01** 0.92

0.001*** 0.11 0.89

0.001*** 0.09 0.73

0.02* 0.68 0.93

0.03* 0.94 0.48

0.05* 0.04* 0.92

Abbreviations: TC, total carbon; TN, total nitrogen; DOC, dissolved organic carbon; TSN, total soluble nitrogen; MBC, microbial biomass carbon; MBN, microbial biomass nitrogen; DNC, Denitrification capacity. Bold value signifies *, ** and *** which indicate the significance at 5%, 1% and 0.1% levels respectively. a B: prescribed burning; S: sampling time. b Significance levels: *P < 0.05, **P < 0.01, ***P < 0.001.

treatment than in the no burning treatments, while there was no significant difference between the 2 yearly burning and the 4 yearly burning or between the 4 yearly burning and the no burning treatments (Table 2). Prescribed burning did not significantly affect MBN, while the microbial C:N ratio was significantly lower in the 2 yearly burning than in the 4 yearly burning treatments in January (Table 2).

3.0 folds higher than in April. In January, the gene with the highest abundance was nirK (4.3  107 to 1.3  108 copies g
1 dry soil) followed by narG (3.3  107 to 5.4  107 copies g
1 dry soil) and nosZ (2.5  107 to 5.0  107 copies g
1 dry soil) (Fig. 2a). No significant difference was observed in the abundance of genes between burning treatments, except for nosZ and nirK genes (Fig. 2a). The copy numbers of nosZ and nirK were significantly lower in the 2 yearly burning treatments than in the 4 yearly burning treatments (Fig. 2a). The abundance of all other functional genes (except for nirS) and 16S rRNA gene tended to be lower in the 2 yearly burning compared with the no burning and 4 yearly burning treatments (Fig. 2a), but the differences were not statistically significant. In April, similar trends in the gene copy numbers were observed across different functional genes and different burning treatments (Fig. 2b).

3.2. In situ N2O flux rate and soil denitrification capacity In situ N2O fluxes ranged from 0 to 8.8 g
1 N2OeN ha
1 h
1 with an average value of 1.47 (Fig. 1). N2O flux rate varied among different months, with the highest rate observed in April and the lowest in July (Fig. 1). Overall, the 2 yearly burning had lower N2O fluxes than the no burning treatments, but these differences were significant only in January and June. There were no significant differences in N2O fluxes between the no burning treatment and the 4 yearly burning except for that measured in June. The denitrification capacity (DNC) was generally lower in the 2 yearly burning treatments compared with other two treatments (Table 2). DNC was positively related to in situ N2O flux for both sampling times but was not statistically significant (P > 0.05) (Table 3).

3.4. Relationships among N2O flux and soil environmental factors and functional gene abundance 3.4.1. Relationships between N2O fluxes and soil environmental factors In situ N2O fluxes were negatively correlated with soil pH (r ¼
0.579 to
0.847, P < 0.05), but positively correlated with soil EC and moisture (Table 3). Similar trends were found between DNC and these soil environmental variables (data not shown). The N2O fluxes were positively and significantly related to concentrations of NO3

N, DOC and TSN, for both January and April sampling times (Table 3), and only significantly related to MBC and MBN in January, but not significantly related to the concentration of NH4 þ
N (Table 3). For bulked two month data, the N2O fluxes were positively and significantly related to NO3

N, DOC, TSN, MBC and MBN (Table 3).

3.3. Abundance of denitrification functional genes Two-way ANOVA shows there was no significant interaction between the sampling time and the treatment (Table 4). The copy numbers of 16S rRNA genes were similar among different treatments within each sampling time, ranging from 6 to 19  108 copies g
1 of dry soil. (Fig. 2a, b). In the no burning and 2 yearly burning treatment, 16S rRNA gene abundance observed in January was 1.5e

Table 2 Selected properties of soils as affected by different prescribed burning regimes at the Peachester site, Queensland, Australia. Parameter

January

pH EC (ms cm
1) Moisture (%) Total C (%) Total N (%) NH4 þ (mg N kg
1) NO3
(mg N kg
1) DOC (mg C kg
1) TSN (mg C kg
1) DOC:TSN MBC (mg C kg
1) MBN (mg N kg
1) Microbial C:N DNC

4.61 39.2 36.9 6.28 0.25 8.02 7.26 730 83.5 8.8 499 72 6.9 40.8

No burning              

0.10(a)a 3.7(a) 2.8(a) 0.58(a) 0.03(a) 1.59(a) 1.58(a) 37(a) 3.9(a) 0.7(a) 41(a) 5(a) 1.0(ab) 7.5(a)

April 2 Yearly burning 5.50 19.7 21.6 3.53 0.10 7.44 0.62 409 41.3 10.0 290 48 6.2 22.8

             

0.12(b) 1.4(b) 1.5(b) 0.25(b) 0.00(b) 0.64(a) 0.27(b) 11(b) 2.9(b) 0.7(a) 24(b) 7(a) 0.4(b) 11.8(a)

4 Yearly burning 5.10 29.5 36.8 6.55 0.30 7.61 4.18 625 68.1 9.4 518 71 7.4 34.7

             

0.18(ab) 4.8(ab) 1.8(a) 0.33(a) 0.00(a) 0.97(a) 1.70(ab) 62(a) 10.0(a) 0.6(a) 41(a) 6(a) 0.1(a) 13.5(a)

No burning 4.56 29.6 37.2 6.23 0.30 6.59 6.75 555 74.8 8.4 426 68 6.3 42.4

             

0.04(a) 2.0(a) 3.7(a) 0.63(a) 0.04(a) 1.14(a) 1.48(a) 23(a) 4.9(a) 0.4(a) 64(a) 10(a) 0.2(a) 5.8(a)

2 Yearly burning 5.20 15.9 23.3 3.70 0.13 7.35 0.50 481 35.2 9.5 264 49 5.4 9.4

             

0.05(b) 1.1(b) 1.3(b) 0.48(b) 0.25(b) 0.79(a) 0.32(b) 12(b) 2.7(b) 0.3(a) 16(b) 4(a) 0.2(a) 5.2(b)

4 Yearly burning 5.07 23.4 37.6 6.83 0.33 9.57 2.94 486 55.6 9.1 422 66 6.5 44.8

             

0.12(b) 2.8(ab) 3.6(a) 0.56(a) 0.25(a) 1.94(a) 1.07(ab) 64(ab) 10.3(ab) 0.8(a) 59(a) 11(a) 0.2(a) 11.0(a)

Abbreviations: DOC, dissolved organic C in hot water extracts; TSN, total soluble N in hot water extracts; MBC, microbial biomass carbon; MBN, microbial biomass N; DNC, denitrification capacity. a Data in table are mean values standard error (n ¼ 12). The different letters in parentheses within a row under the same sampling month indicate significant differences between the burning treatments (P < 0.05).

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X. Liu et al. / Soil Biology & Biochemistry 57 (2013) 292e300

-1

-1

N2O emission (g ha h )

7

aaa

NB

6

2YB

5

4YB

4

a b ab aaa

3

aaa

2

abb

1 0 N JA

M

AR

R AP

M

AY

N JU

aaa L JU

Fig. 1. In situ measurement of soil N2O fluxes under different fire regimes over the period of JanuaryeJuly 2011 (no data available for February) at the Peachester site, Queensland, Australia. NB, no burning; 2YB, 2 yearly burning; 4YB, 4 yearly burning. Mean values and standard error are shown (n ¼ 12). The different letters above the bars indicate significant differences between the treatments within each sampling month (P < 0.05).

3.4.2. Relationships between N2O fluxes and functional gene abundance Pearson correlation analysis was performed for N2O fluxes and the log-transformed copy numbers of denitrification functional genes. No significant relationships were found between the abundance of any denitrification functional genes and N2O fluxes in January (Table 4), while narG was positively correlated to N2O flux (r ¼ 0.527 and 0.638, n ¼ 12, respectively) in April (Table 4). 3.4.3. Relationships between functional gene abundance and soil environmental factors Canonical Correspondence Analysis (CCA) using data across both sampling times, found that CCA1 and CCA2 account for ca. 89.1% of total variation in gene abundance (Fig. 3). Geneenvironment correlations for the first two axes were 0.770 and 0.723 respectively, indicating the abundance of denitrification Table 3 Pearson’s correlation coefficients for relations between soil parameters (including abundance of denitrification genes) and in situ N2O flux. Parameter

pH EC Moisture TC DOC MBC TN NO3
NH4 þ TSN MBN MBC/N ratio DOC/TSN ratio DNC narG nirS nirK nosZ

Pearson’s correlation coefficients January (n ¼ 12)

April (n ¼ 12)

January þ April (n ¼ 24)

L0.847** 0.813** 0.747** 0.616* 0.863** 0.755* 0.660* 0.797**
0.025 0.875** 0.744** 0.297
0.481

L0.579* 0.631* 0.424 0.314 0.711** 0.543 0.381 0.937**
0.524 0.604* 0.516 0.227
0.252

L0.664** 0.420* 0.516** 0.404 0.500* 0.431* 0.477* 0.655** 0.234 0.531** 0.507*
0.029
0.377

0.500 0.042
0.244 0.183 0.266

0.422 0.527
0.304 0.459 0.448

0.400 0.313
0.309 0.192 0.331

Abbreviations: TC, total carbon; TN, total nitrogen; DOC, dissolved organic carbon in hot water extracts; TSN, total soluble nitrogen; MBC, microbial biomass carbon; MBN, microbial biomass nitrogen; DNC, denitrification capacity. Bold value signifies * and ** which indicate the significance at 5% and 1% levels respectively. Significance levels: *P < 0.05, **P < 0.01.

Table 4 P values from Two-Way ANOVA of effects of prescribed burning (B), sampling time (S) and their interaction (B  S) on soil denitrification gene abundance at the Peachester site, Queensland, Australia. Sourcea

df

Bacterial 16S rRNAb

narG

nirK

nirS

nosZ

B S BS

2 1 2

0.04* 0.0000*** 0.17

0.06 0.92 0.40

0.05* 0.26 0.56

0.81 0.59 0.43

0.20 0.95 0.35

Bold value signifies *, ** and *** which indicate the significance at 5%, 1% and 0.1% levels respectively. a B: prescribed burning; S: sampling time. b Significance levels: *P < 0.05, **P < 0.01, ***P < 0.001.

genes was strongly influenced by the environmental factors. The arrows for soil EC, DOC and NO3
were longest, followed by TSN, TC, moisture content, pH, TN and NO4
, and the shortest one was DOC:TSN ratio, indicating EC, DOC and NO3
accounted for the greatest proportion of variances in the denitrification gene abundance. Bacterial abundance was positively influenced predominately by EC, DOC and TSN (Fig. 3), while the abundance of nirS was positively affected by soil pH, but negatively by many other soil environmental factors (NO3
, EC, DOC, TSN, TC, moisture and TN etc.). The abundance of nosZ, narG and nirK was closely and positively related to NO4
, but also affected by other soil environmental variables (Fig. 3). 3.5. PCA of N2O flux, soil environmental factors and functional gene abundance Results from the PCA of soil basic properties, N2O flux, and functional gene data showed that soil samples in the 2 yearly burning treatments were clearly separated from those in the 4 yearly burning and the no burning treatments along the PC1 in January (Fig. 4a). On the other hand, the soil samples in the 4 yearly burning treatment were not distinctly separated from those in the no burning treatment (Fig. 4a). The sum of PC1 and PC2 accounted for 78.1% of the variation in soil parameters measured in January (Fig. 4c). Soil environmental factors (contribution > 5% to PC 1), including moisture, TN, TC, DOC, TSN, NO3
, MBC, MBN, EC, pH and N2O fluxes, contributed significantly to the variation of PC1 (76.0%) (Table S2), while none of bacterial 16S rRNA, nirK, nirS, nosZ, and narG genes contributed over 5% (Table S2). In April, soil samples in the 2 yearly burning treatments were also well separated from the other two treatments, but along both PC1 and PC2 (Fig. 4b). Moreover, soil samples in the 4 yearly burning treatments were further separated from those in the no burning treatments, despite large variations observed among replicates of 4 yearly burning samples (Fig. 4b), while the corresponding (sum of PC1 and PC2) value for April was 72.3% (Fig. 4d). The soil environmental factors whose individual contribution was above 5% accounted for 55.0% of the variation of PC1 (Table S2), while bacterial 16S rRNA, nosZ, and narG genes contributed over 5%, accounting for 18.2% (Table S2). 4. Discussion 4.1. Impacts of prescribed burning on N2O fluxes The magnitudes of N2O fluxes (1.47 g
1 N2OeN ha
1 h
1 in average) measured in this study were generally comparable to those from other forest soils (Goreau and De Mello, 1988; Serca et al., 1994; Breuer et al., 2000). For example, it was reported that soil N2O fluxes varied in a range of 0.04e0.70 g N2OeN ha
1 h
1 for neotropic rain forests (Goreau and De Mello, 1988; Keller et al., 1993; Verchot et al., 1999), 0.02e2.07 g N2OeN ha
1 h
1for an African rain forest site in Congo (Serca et al., 1994), 0.04e0.11 g

10

NB 2YB 4YB

aaa

bba

-1

9

a

log of gene copy number g dry soil

-1

log of gene copy number g dry soil

X. Liu et al. / Soil Biology & Biochemistry 57 (2013) 292e300

ab b a

aaa

8

aaa 7 6 5

nirS

narG

A rR N 16S

nirK

nosZ

297

NB 2YB 4YB

10 aaa 9 a b ab

8 7

b aaa

aaa

aaa

6 5 rR 16S

NA

narG

nirS

nirK

nosZ

Fig. 2. Abundance of the bacterial 16S rRNA, narG, nirS, nirK, and nosZ genes in soils under different burning regimes in January (a) and April (b), expressed as the number of gene copies g
1 dry soil. NB, no burning; 2YB, 2 yearly burning; 4YB, 4 yearly burning. The different letters above the bars indicate significant differences between the treatments within each sampling month (P < 0.05).

0.8

N2OeN ha
1 h
1for forest peatlands in Indonesia (Takakai et al., 2006) and 0.11e1.23 g N2OeN ha
1 h
1 for Australian rain forests (Breuer et al., 2000). In general, prescribed burning did not significantly affect soil N2O fluxes in grassland ecosystems (e.g. Castaldi et al., 2010; Livesley et al., 2011). However, little work has been done on the effects of fire on N2O fluxes in forest ecosystems. Takakai et al. (2006) compared N2O fluxes in peatlands under natural, regenerated and burnt forests and found that there were no significant differences among these forests in the first year of measurements, but N2O fluxes were lower in the burnt forest in the second year. This was ascribed to the difference in rainfall between the years (Takakai et al., 2006). Levine et al. (1988) failed to detect N2O fluxes before or after a prescribed burn in California chaparral, and only after wetting the soil fluxes were detected. Dannenmann et al. (2011) reported that in Mediterranean shrublands N2O fluxes were higher in the unburnt treatments than those in the burnt

treatments which were close to zero. It has been suggested that substrate availability may not be the major limitation to N2O fluxes, but rather soil moisture is the limiting factor in certain dry ecosystems (Gathany and Burke, 2011). Most of these studies investigated the short-term impacts of fires on N2O emissions, while the long-term impacts of fires have been the major focus of this present study. Results from this study have shown that the 2 yearly burning treatment had lower in situ N2O fluxes than the no burning treatments, while there were no significant differences between the 4 yearly burning and no burning treatments (Fig. 1). DNC also showed similar trends to the in situ N2O fluxes among the burning treatments (Table 2). These results indicate that the frequency of long-term repeated prescribed fires had significant impacts on the denitrification and N2O fluxes. In addition, the extent of effects of fires on N2O fluxes varied slightly with the sampling month (January and April). This may be ascribed to the differences in moisture and temperature conditions between the two months, which may have influenced the soil environmental factors and microbial functional groups.

bacteria

4.2. Impacts of prescribed burning on the quantity of denitrification functional genes

EC DOC TSN

NO3-

Axis 2

moisture TC

DOC:TSN ratio

TN nirK

pH

NH4+

nirS nosZ

-0.6

narG

- 1.0

Axis 1

1.0

Fig. 3. Canonical Correspondence Analysis (CCA) of relationships between abundance of functional genes and the environmental factors, using the bulked January and April data. The first two dimensions CCA1 and CCA2 represent the relationship between gene abundance, substrate availability and environmental factors. The arrow points in the direction of maximum change in the value of the soil environmental variable, while the arrow length is proportional to this maximum rate of change, indicating the importance of the variable. The percentage variance of functional geneesoil environment relation for CCA axes 1 and 2 are 49.7% and 39.4% respectively. EC, electrical conductivity; DOC, dissolved organic C; TSN, total soluble organic N; TC, soil total C; TN, soil total N.

The abundance of denitrification functional genes can be affected by elevated CO2 (e.g. Long et al., 2012a,b), warming (e.g. Long et al., 2012a,b), nutrient enrichment (e.g. Lindsay et al., 2010) and land use change (e.g. Yao et al., 2011). Although some studies have addressed the impacts of fire on the composition of soil bacteria and fungal communities (e.g.; Campbell et al., 2008; McMullan-Fisher et al., 2011), no studies to date have determined the effects of fires on the abundance of denitrification functional genes. In the present study, 2 yearly burning significantly reduced soil microbial biomass compared with no burning and 4 yearly burning (Table 2), but did not affect the quantity of bacterial 16S rRNA gene (Fig. 2). Studies from the same site have found that fungal communities were greatly affected by the burning treatment (Bastias et al., 2006). These results have suggested that bacteria may be more resistant to fire than fungi (González-pérez et al., 2004; Hart et al., 2005). The different denitrification functional genes responded differently to the different burning treatments (Fig. 2). Fire treatments only affected a small number of microbial functional genes (e.g. nirK and nosZ) (Fig. 2). Overall, no significant differences in the abundance of most denitrification genes (except for nirK in January) between the 4 yearly burning and no burning treatments suggests that the soil microbial community has capacity to recover in the longer fire intervals (4 years).

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Fig. 4. Scores plot of principal component analysis (PCA) showing: the separation of different prescribed burning regimes for January (a) and April (b) samples; and loading values of the individual soil parameter for PC1 and PC2 for January (c) and April (d) samples. The hollow circles represent soil samples from three prescribed burning treatments (NB, no burning; 2YB, 2 yearly burning; 4YB, 4 yearly burning) and four replicate plots (1e4).

On the other hand, the abundance of nosZ and nirK genes in the 4 yearly burning treatments was greater compared with no burning and the 2 yearly burning treatments in January, but this was not significant in April (Fig. 2a). This indicates the effects of fire regimes on these denitrification genes may also vary with seasonal environmental conditions. These diverse impacts of fire regimes on soil denitrification functional genes might have been due to the heatinduced shift in microbial mortality and the difference in fire resistance among microbial functional groups (e.g. Hart et al., 2005). In addition, over the long-term, fire may modify the soil microbial community by altering plant community composition via plant-induced changes (e.g. C allocation) in the soil environment (Papanikolaou et al., 2010). Results of the CCA from this study have shown a clear link between soil environmental factors and the abundance of denitrification genes (Fig. 3). The long-term fireinduced changes in key soil environmental parameters (e.g. soil EC, DOC, NO3
, TSN, TC, TN, moisture, pH and NH4 þ ) explained the majority of the variation in the abundance of denitrification genes (Fig. 3). 4.3. Links of N2O flux to denitrification genes and soil environmental factors Production of N2O is essentially mediated by microbial functional groups, however, in many cases the abundance of

 denitrification genes may not be related to the N2O flux (e.g. Cuhel et al., 2010). In the present study, the abundances of most denitrification genes (e.g. narG, nirS, nirK and nosZ) were not correlated with N2O fluxes (Table 3) and DNC (data not shown), indicating the abundance of these genes might not be limiting factors. Denitrifying community is such a highly diverse community, consisting of archaea, bacteria and fungi, that it exhibits great functional redundancy (Wertz et al., 2007). Therefore, the uncoupling between denitrification gene abundance and N2O flux is understandable. On the other hand, results from this study have shown that soil environmental factors (moisture, pH, EC and concentration of NO3
, DOC and TSN) were more significantly related to N2O fluxes than dentrification gene abundance (Table 3). This indicated that fire-induced shifts in substrate availability and soil environmental factors were largely responsible for the variation in N2O fluxes across different fire regimes. Denitrification rate is generally considered to increase with soil  pH in acidic soils (e.g. Weslien et al., 2009; Cuhel et al., 2010). However, in the present study, DNC decreased with increasing pH (r ¼
0.588, P < 0.01, n ¼ 24, across January and April data). This was probably because soil DOC and TC, which generally enhanced DNC, were lower under the 2 yearly burnt treatment that had higher pH values. It has been reported that N2O production from both autotrophic and heterotrophic nitrification increased as pH decreased (Martikainen, 1985; Martikainen and de Boer, 1993).

X. Liu et al. / Soil Biology & Biochemistry 57 (2013) 292e300

Significant and negative correlations between in situ N2O fluxes and soil pH (4.4
5.9) (Table 3) indicated that increased pH as a result of enhanced ash inputs in the 2 yearly burning treatments was likely to be partly responsible for lower N2O fluxes. Previous studies have also shown that soil pH was negatively related to the N2O flux due to the inhibition of nitrous oxide reductase by acidic pH (e.g. Skiba et al., 1993; Dannenmann et al., 2008; Weslien et al., 2009). The lower levels of soil EC, moisture, NO3
, DOC, TSN and microbial biomass found in the 2 yearly burning treatments compared with the no burning treatments (Table 2) and the strong relationships between the N2O fluxes and these parameters (Table 3) indicated lower substrate and water availability in the 2 yearly burning treatments also contributed to the lower N2O fluxes. Results from the PCA of all soil environmental and functional gene parameters (Fig. 4; Table S2) also indicated that soil environmental factors contributed >76% of variation in PC1 while total contributions of bacterial abundance, nirK, nirS, nosZ and narG to the variation in PC1 accounted only for <24%. This further confirmed that substrate availability and soil environmental factors rather than the abundance of denitrification genes are the predominant regulators in N2O production in response to fire. This is in agreement with the observation of Attard et al. (2011) who reported that potential denitrification was more related to changes in soil environmental conditions than in denitrifier abundance after land-uses changes. 5. Conclusions Our findings have demonstrated that more frequent fire (2 yearly burning) reduced N2O fluxes and availability of C and N substrate and water, and increased soil pH and EC compared with no burning and 4 yearly burning treatments. The abundance of most denitrification genes was not different among the fire treatments. On the other hand, there were no significant differences in most soil parameters and denitrification gene abundance measured between the no burning and 4 yearly burning treatments, indicating that soil microbial community has capacity to recover in the longer fire intervals (4 years). Soil substrate (NO3
, DOC and TSN) availability and soil environmental factors (pH, EC, and moisture) largely explained the variation in the N2O fluxes among the treatments. It is concluded that soil environmental factors rather than denitrification gene abundance control soil N2O flux. Further research is required to investigate the expression of the denitrification gene abundance and its relations to N2O fluxes and to separate N2O produced through denitrification from auto- and hetero-trophic nitrification. Acknowledgement We would like to thank Marijke Heenan, Hannah Toberman, Junqiang Zheng, and Xien Long for their assistance in the field sampling and Steven Reeves for gas analysis. This research was supported under Australian Research Council’s Future Fellowship (project number FT0990547). Xian Liu is supported by a Griffith University Postgraduate Research Scholarship. Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.soilbio.2012.10.009. References Attard, E., Recous, S., Chabbi, A., Berranger, D.C., Guillaumaud, N., Labreuche, J., Philippot, L., Schmid, B., Roux, X.L., 2011. Soil environmental conditions rather

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