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iForest - Biogeosciences and Forestry
vol. 9, pp. 813-821
Copyright © 2016 by the Italian Society of Silviculture and Forest Ecology
doi: 10.3832/ifor1828-009

Research Articles

Seasonal dynamics of soil respiration and nitrification in three subtropical plantations in southern China

Weixia Wang (1-2)Corresponding author, Ruimei Cheng (2-3), Zuomin Shi (2-3), Joachim Ingwersen (4), Da Luo (2), Shirong Liu (2)

Introduction 

Forests are important carbon (C) pools in terrestrial ecosystems and play an important role in the global C and nitrogen (N) cycle. Soil CO2 efflux (soil respiration) is sensitive to climate, vegetation type and soil properties ([41]). Soil temperature and soil water content are recognized as the main factors controlling soil respiration ([24]), whereas the soil biophysical environment and substrate availability (e.g., aboveground and belowground litter fall, soil organic C) are the main factors controlling heterotrophic respiration ([45], [32]).

The N cycle in forests is a complex system with close linkages and interactions between soil, plants and microbes. The natural N supply for plants and microorganisms is derived from the mineralization of organic N compounds ([16]). This process occurs in two steps, ammonification and nitrification, which play a key role by making inorganic N compounds available for plants and microbes. The process is influenced by a number of factors such as composition and diversity of the soil microbial community, substrate quality and quantity, and environmental conditions (temperature and water content - [55], [18]). These factors are influenced by tree species and plantations. In fact, tree species are known to affect the physicochemical and biological characteristics of soils ([7], [49]). Soil microbial biomass (MB), activity, and community structure might thus be tree species dependent ([19]). Tree species also differ in the quality of leaf litter (e.g., C/N), which directly influences the quality and quantity of organic matter input ([55], [12]). Moreover, root litter is also species dependent and directly affects root exudation ([13]). Accordingly, microbes receive organic matter of varying quality from different tree species ([55]). This, in turn, may lead to changes in soil microbial communities and MB ([5]), which subsequently influence soil N transformations ([42], [53]). Most studies on microbial N cycling have concentrated on determining net nitrification rates ([38], [40], [62]) rather than gross rates. Direct comparisons of net and gross nitrification rates, however, reveal that the former can be one order of magnitude lower than the latter. Thus changes in the NO3- pool do not necessarily reflect the total turnover from NH4+ to NO3- ([52], [39], [22]). These findings highlight the importance of measuring gross nitrification rates to understand N transformation in forest ecosystems.

Only few studies have investigated gross nitrification in soil under different tree species/forest types. Brüggemann et al. ([12]) studied soil respiration, gross N mineralization and gross nitrification in pure stands of different temperate tree species. Matejek et al. ([34]) and Rosenkranz et al. ([44]) investigated ammonification and gross nitrification in forest soil layers in southern Germany (temperate climate). Comparable comprehensive studies are missing for subtropical climate regions.

Southern China, which is located mostly in the subtropical region, has 25 million hectares of plantations ([59]). Plantations are becoming a key component of the world’s forest resources and play an important role in the context of sustainable forest management ([60]). This calls for studies on soil C and N transformation under the main tree species used for afforestation to better understand the C and N cycle of subtropical plantation ecosystems.

Accordingly, the objective of this study was to determine the seasonal dynamics of soil respiration and nitrification in the top soil layer in subtropical plantations and to elucidate the relationships of these two key turnover processes with the composition of the soil microbial community (bacteria vs. fungi) and the two environmental factors soil temperature and soil water content.

Materials and methods 

Study area

The study was conducted at the Experimental Center of Tropical Forestry, the Chinese Academy of Forestry (22° 06′ N, 106° 46′ E), Pingxiang City, Guangxi Zhuang Autonomous Region, China. Annual rainfall is about 1400 mm and occurs primarily from April to September. The annual mean temperature is 21 °C, the mean monthly minimum is 12.1 °C, and the mean monthly maximum is 26.3 °C. The soil was formed on granite, classified as red soil in Chinese soil classification, equivalent to Oxisol in USDA Soil Taxonomy ([27], [59], [21]).

We studied three of the most dominant plantations: one conifer plantation (Pinus massoniana Lamb.) and two broadleaved plantations (Castanopsis hystrix Miq. and Erythrophleum fordii Oliv.). E. fordii is a N-fixing species. These monoculture plantations were selected based on their similar topography, soil texture, stand age and management history. The three plantations were established after a clear-cut harvest of P. massoniana plantation in 1978, at an elevation of 350 m, over areas ranging from 2.2 to 4.8 hectares. Stem density varied from 410 to 415 trees per hectare, the diameter at breast height (DBH) ranged from 22.4 to 26.4 cm. In each plantation, five sampling squared plots (20 × 20m) were randomly selected and delineated.

Soil sampling

Soil was sampled monthly over a period of 11 months (from August 2011 to July 2012; except January, the date of sampling was the same every month, i.e., 15th), covering the entire season, i.e., wet, dry and intermediate conditions. Five intact soil cores were randomly taken from the top soil layer using a soil corer (5.6 cm diameter, 4.1 cm height) at each plot after removing the litter ([25], [60], [61]). The intact soil cores were analyzed for gross nitrification and soil respiration rates using the BaPS technique (UMS GmbH Inc., Germany - [22], [11], [26]).

Bulk soil samples for chemical analysis were collected from the top soil layer (0-5 cm) in late February 2012 (dry season) and July 2012 (wet season). A total of six soil cores per plot were randomly collected using a 5.0-cm-diameter stainless steel core and bulked to one composite sample. After collection, samples were immediately delivered to the laboratory for further analysis. In the lab, each composite sample was passed through a sieve (2 mm mesh size), and plant material was manually removed from the sieved soil. The sieved soil was divided into three subsamples. The first subsample was used to determine soil organic carbon (SOC), total nitrogen (TN) and soil pH. The second subsample was kept at 4 °C for analysis of nitrate (NO3--N), ammonium (NH4+-N), microbial biomass carbon (MBC) and microbial biomass nitrogen (MBN). The third subsample was used for total PLFAs, fungal and bacterial PLFAs determinations, and was stored at -20 °C.

Physicochemical analyses

Soil samples collected for physicochemical analyses were ground to pass through a 0.25 mm sieve. SOC was measured using the potassium dichromate vitriol oxidation method ([28]) and TN concentration was measured after semimicro-Kjeldahl digestion using a flow injection auto-analyzer (FIA®, Lachat Instruments, USA). Soil C/N values were calculated as the ratio of SOC to TN. Soil pH was determined using a 1:2.5 soil/water suspension. Inorganic N (ammonium and nitrate) was extracted with 2 M KCl solution. Ammonium and nitrate in extract were measured using the above flow injection auto-analyzer. Soil MBC and MBN were determined using the fumigation-extraction method ([58], [54]). Specifically, soil samples from each experimental site were divided into paired subsamples of 20 g. One subsample was immediately extracted with 60 ml 0.5 M K2SO4. The second subsample was fumigated with chloroform vapor for 48h in a desiccator followed by 10 vacuum/purge cycles, and then extracted as described above. Soil extractable organic C and TN in the K2SO4 extracts before and after the fumigation were quantified using a total C/N analyzer (Multi-N/C 2100®, Analytik Jena AG, Germany). The released C and N were converted to MBC and MBN, respectively, using the conversion factors Kec=0.45 and Ken=0.45. Soil temperatures at 5 cm depth (T5) were determined by a digital thermometer concomitantly with the soil samplings. All results were expressed per unit of oven-dried soil weight.

Phospholipid fatty acid extraction

The soil microbial community was characterized using PLFA analysis as described by Bossio & Scow ([10]). Lipids were extracted from 8 g of dry-weight-equivalent fresh soil with a one-phase mixture of chloroform, methanol and phosphate buffer (1:2:0.8). The separated fatty acid methyl-esters were re-dissolved in 200 µl hexane containing 19:0 as an internal standard and were analyzed using a Hewlett-Packard 6890 Gas Chromatograph equipped with an Ultra 2-methyl polysiloxane column. Concentrations of each PLFA were calculated based on the 19:0 internal standard concentrations. The abundance of individual fatty acids was determined as nmol per g of dry soil and standard nomenclature was used ([57]).

Bacteria were considered to be represented by 12 PLFAs (i14:0, i15:0, a15:0, 15:0, i16:0, 16:1ω7c, i17:0, a17:0, 17:0, cy17:0, cy19:0, 18:1ω7c), and gram-positive bacteria were identified by the PLFAs i14:0, i15:0, a15:0, i16:0, i17:0, a17:0, gram-negative bacteria by the PLFAs 16:1ω7c, cy17:0, cy19:0. The fungi were considered to be represented by the PLFAs 18:2ω6.9c and 18:1ω9c ([2], [20]). A ratio of the gram-positive bacteria to the gram-negative bacteria PLFAs (G+/G-) was used as an indicator of changes in the relative abundance of these two microbial groups; the ratio of 18:2ω6.9c and 18:1ω9c to total bacterial PLFAs was used to estimate the ratio of fungal to bacterial biomass (F/B) in soils ([4]). Several other PLFAs such as 16:0, 16:1 2OH, 16:1ω5c, 10Me16:0 and 10Me17:0 were detected and also used to analyze the composition of microbial community.

Determination of gross nitrification and soil respiration

The BaPS technique enables simultaneous determination of gross nitrification and soil respiration ([22], [23]). Previous studies testing the BaPS method against the 15N pool dilution technique revealed that using the default value of the respiration quotient (RQ) of unity tends to overestimate gross nitrification rates ([37], [43]). Müller et al. ([37]) thus suggested using the 15N pool dilution technique for determining the RQ value needed in the BaPS method. For acid forest soils, Rosenkranz et al. ([43]) and Matejek et al. ([35]) found average RQ values of 0.90 ± 0.01 and 0.89 ± 0.02, respectively. We therefore used a RQ of 0.9 in the present study. The autotrophic-to-heterotrophic nitrification ratio was kept at the default ratio of 3:1 ([22], [23]).

Gross nitrification and soil respiration rates were determined by incubating intact soil cores (5.6 cm diameter, 4.1 cm height), which were taken from the top soil from each sampling spot. Five replicates per experimental plantation were taken at each sampling date. Immediately after sampling, the undisturbed soil cores were sealed with parafilm and transported to the laboratory, where they were stored in the dark at in situ soil temperature as determined by use of Pt100 probes at the field site at 2 pm. Soil samples were incubated in the BaPS incubation chamber for 24h at stable in situ soil temperatures. At the end of the incubation, soil water content and bulk density were determined gravimetrically by drying soil samples at 105 °C for 24h. Water-filled pore space (WFPS) was calculated using the following formula ([56] - eqn. 1):

\begin{equation} WFPS = {\frac{ SWC} {1-{\frac{ BD} { PD}} }} \end{equation}

where SWC is the volumetric water content (cm3 cm-3), BD is the soil bulk density (g cm-3), and PD denotes the soil particle density, which was assumed to be 2.65 g cm-3 ([56], [11]).

Statistical analysis and data evaluation

Statistical analyses to determine significant differences between plantations and sampling dates were performed using SPSS® 16.0 and SigmaPlot® 10.0 (SPSS Inc., Chicago, USA). To test for normal distribution of data we used the normal probability plot. For the analyses of variance we used a one-way analysis of variance (ANOVA) with a least significant differences (LSD) post hoc test. Correlations between gross nitrification and soil respiration were also analyzed by bivariate linear regressions.

We used an exponential equation ([31]) to describe the response of the two turnover processes to temperature (eqn. 2):

\begin{equation} k(T) = a \cdot exp(bT) \end{equation}

where k is the turnover rate of soil respiration or gross nitrification, T is soil temperature measured at 5 cm depth, and a and b are regression coefficients. The temperature sensitivity (Q10) was calculated as ([31] - eqn. 3):

\begin{equation} Q_{10} = exp(10 \cdot b) \end{equation}

The effect of WFPS on the two turnover processes was evaluated with a simple linear regression ([30]) as follows (eqn. 4):

\begin{equation} k(WFPS) = cWFPS + d \end{equation}

where c and d are the slope and the intercept, respectively.

We incubated the soil samples under in situ conditions, i.e., for incubation we used the temperature and the WFPS measured at the field site. To disentangle the effect of differing temperature and WFPS between the sites from other factors such as vegetation type or soil physicochemical properties, the rates were normalized for each month to the mean temperature and mean WFPS used in the incubations (eqn. 5):

\begin{equation} k^\text{*} = k \cdot exp[b(T - \overline{T})] {\frac{c \overline{WFPS} + d} { c \overline{WFPS} + d }} \end{equation}

where k is the measured rate, and k* denotes the normalized rate, T and {bar}T are the site and mean temperature (N=3), respectively. The same notation was used for WFPS.

The composition of the soil microbial community was summarized using principal component analysis (PCA) on the 19 individual PLFAs (nmol g-1 dry soil) from the PLFA analysis of soil samples after standardization for equal unit variance. To test for normal distribution of data we used the normal probability plot. Differences in individual soil PLFA among plantations and seasons were tested with one-way analysis of variances (ANOVA).

Results 

Soil carbon and nitrogen pools

Soil properties including SOC and TN varied significantly among the plantations (Tab. 1). In particular, the TN content of the top soil in the plantation of the N-fixing species E. fordii was substantially higher than in the other two plantations. SOC in the E. fordii plantation was significantly higher than that in P. massoniana, although it was not significantly different from C. hystrix. There were no significant seasonal differences of the topsoil TN content among the plantations, while the SOC difference between dry and wet seasons was significant in the E. fordii and the C. hystrix plantations.

Tab. 1 - Soil pH and carbon and nitrogen pools of three subtropical plantations in Southern China. Data are means ± standard error (n=5). A one-way analysis of variance (ANOVA) with a least significant differences (LSD) post-hoc test was used. Different letters indicate significant differences (P<0.05) among plantations (lower case letters) or between different seasons (upper case letters).

Soil C to N ratios and nitrate concentrations varied significantly between the N-fixing vs. the non-N-fixing plantations (P < 0.05). Soil ammonium concentrations were the highest in the E. fordii plantation, where the soil pH values were the lowest.

Soil respiration rates

Soil respiration rates were variable over the two seasons in all the plantations. The minimum respiration rates occurred during the dry season, when soil water content decreased to values below 45.0% WFPS. The maximum respiration rates were measured during the wet season when soil water content increased from 50.0% to 78.5% WFPS (Fig. 1). Soil respiration rates were not significantly different among the plantations (P>0.05). In the dry season, the rates were 3-fold to 5-fold lower than in the wet season (Fig. 1).

Fig. 1 - Means (± SE) of soil temperature, WFPS, soil respiration and gross nitrification in the three plantations at different sampling dates over 2011-2012. A one-way analysis of variance (ANOVA) with a least significant differences (LSD) post-hoc test was used. Different letters indicate significant differences (P<0.05) among the plantations for a given sampling date. All rates were normalized to the mean temperature and WFPS of each month.

In all the three plantations, soil respiration increased with increasing temperature and WFPS (Fig. 2). The response to temperature was well described by the exponential function (eqn. 2). The coefficient of determination ranged from 0.76 to 0.86 (Fig. 2a). Based on the regression coefficient b of eqn. 2, Q10 values of respiration were 2.87, 3.98 and 2.70 in the E. fordii, C. hystrix and P. massoniana plantations, respectively. Moreover, we found a linear relation between soil respiration and WFPS (R2 = 0.31-0.56, P < 0.0001 - Fig. 2b).

Fig. 2 - Relation between soil respiration rate and (a) soil temperature, or (b) water-filled pore space (WFPS) for the three plantations. Soil temperature was measured at 5 cm depth and soil water content was measured at 0-5 cm depth.

Gross nitrification rates

Gross nitrification rates showed a seasonal pattern similar to that of soil respiration (Fig. 1). The highest rates were recorded during the wet season and the lowest rates during the dry season. In the wet season, rates were up to 11-fold higher than in the dry season in the P. massoniana plantation (Fig. 1). Over the entire observation period, mean gross nitrification ranged between 0.11 and 2.06 mg N kg-1 SDW d-1. The differences among the plantations were not significant (Fig. 1).

The response of gross nitrification to temperature was well described with the exponential approach (R2 between 0.54 and 0.79, P < 0.0001 - Fig. 3a). The data, however, scattered much more strongly around the fit compared with the soil respiration data. The calculated Q10 values were 3.39, 4.85 and 8.25 for E. fordii, C. hystrix and P. massoniana, respectively. Gross nitrification showed a weak linear relationship to WFPS (R2 = 0.14-0.32, P < 0.001 - Fig. 3b).

Fig. 3 - Relation between gross nitrification rate and (a) soil temperature, or (b) water-filled pore space (WFPS) for the three plantations. Soil temperature was measured at 5 cm depth and soil water content was measured at 0-5 cm depth.

Significant linear correlations between soil respiration and gross nitrification were found in all three plantations and the strongest correlation was in the E. fordii plantation (Fig. 4).

Fig. 4 - Linear correlation between gross nitrification rate and soil respiration rate for the three plantations.

Soil microbial community composition

Total PLFAs did not differ among the plantations, while the abundance of individual PLFAs did (Tab. 2). In the wet season, the abundance of gram-positive bacteria (i15:0, a15:0, i16:0, i17:0, a17:0), gram-negative bacteria (16:1ω7c, cy17:0, cy19:0) and ectomycorrhizal fungi (18:1ω9c) was higher in the C. hystrix plantation than in the E. fordii plantation (P < 0.05). Saprophytic fungi (18:2ω6.9c), arbuscular mycorrhizae fungi (16:1ω5c) and the ratio of Gram+ to Gram- differed significantly among the three plantations for two seasons; saprophytic fungi (18:2ω6.9c) and arbuscular mycorrhizae fungi (16:1ω5c) were higher in the dry season for all the plantations (Tab. 2).

Tab. 2 - Abundance of soil indicator lipids in the dry and wet season in the three plantations. Data are means ± standard error (n=5). A one-way analysis of variance (ANOVA) with a least significant differences (LSD) post-hoc test was used. Different letters indicate significant differences (P<0.05) among plantations (lower case letters) or between different seasons (upper case letters).

Principal Components Analysis of the microbial community composition, defined by the 19 PLFAs, showed that the first principal component (PC1) accounted for 65.8% and the second component (PC2) for 20.6% of the total variation in microbial communities (Fig. 5). The C. hystrix plantation was clearly separated from the other two on the PC1 axis (Fig. 5). This difference was mainly caused by the relatively higher abundances of Gram-positive bacteria, Gram-negative bacteria, saprophytic fungi and ectomycorrhizal fungi in the C. hystrix plantation. The E. fordii plantation was clearly separated from the P. massoniana plantation on the PC2 axis (Fig. 5) because the E. fordii plantation showed lower abundances of ectomycorrhizal fungi, arbuscular mycorrhizae fungi and Gram-negative bacteria (Tab. 2).

Fig. 5 - Principal component analysis of phospholipid fatty acid (PLFA) structures in the soils of the three plantations.

Microbial community and its relation to C and N turnover

Soil MB, total PLFAs, fungal PLFAs, bacterial PLFAs, soil respiration and gross nitrification rates were significantly different between the dry and wet season, while the latter two showed a seasonal pattern opposite to that of soil MB, total PLFAs, fungal PLFAs and bacterial PLFAs (Tab. 3). While soil respiration and gross nitrification rates were higher during the wet season, soil MB, total PLFAs, fungal PLFAs and bacterial PLFAs reached their lowest values during the same period.

Tab. 3 - Microbial, fungal and bacterial biomass along with soil respiration and gross nitrification rates of the top soil in the three plantations. Data are means ± standard error (n=5). A one-way analysis of variance (ANOVA) with a least significant differences (LSD) post-hoc test was used. Different letters indicate significant differences (P<0.05) among plantations (lower case letters) or between different seasons (upper case letters). Soil respiration and gross nitrification rates were normalized to the mean temperature and WFPS.

The highest MBC values were in C. hystrix, whereas the highest MBN values were in E. fordii for both dry and wet seasons. The P. massoniana plantation showed the lowest MBC and MBN contents (Tab. 3). Two PLFAs (18:2ω6.9c and 18:1ω9c) were used as indicators of fungal biomass. Both PLFAs varied among the plantations. The highest fungal PLFAs were recorded in the C. hystrix plantation, followed by P. massoniana and E. fordii. Moreover, the highest amount of bacterial PLFAs was in the C. hystrix plantation, and the lowest was in the P. massoniana plantation. PLFA-derived F/B ratios did not differ significantly among the plantations (Tab. 3).

Discussion 

Soil respiration and gross nitrification in all the three plantations showed a pronounced seasonal pattern with significantly higher rates during the wet versus the dry season (Fig. 1 and Tab. 3). These findings are consistent with previous reports on the significant seasonal variation of soil respiration and gross nitrification rates in different geographic regions and different tree species ([12], [25], [64], [44], [29]).

In all the three plantations, soil respiration rate was significantly related to soil temperature and was well described by an exponential function within the temperature range observed in the field (Fig. 2a). This relation has been often described in the literature ([11], [44], [36]). Regression analysis showed that the observed variance of soil respiration was largely explained by soil temperature (R2=0.76-0.86; Fig. 2a). These values correspond very well to data of Luan et al. ([31]) in a warm-temperate forest ecosystem (R2 = 0.83-0.93). Our results also showed that the gross nitrification rate was positively correlated with soil temperature (Fig. 3a). Ingwersen et al. ([22]) found the highest gross nitrification at 25 °C, with a Q10 value of 4.13 for the temperature range between 15 and 25 °C for coniferous forest in Germany. In the present study, the highest gross nitrification rate was recorded at 27.7 °C, and a Q10 value of 5.49 was calculated within the temperature range from 16 to 26 °C. This is somewhat higher than the value obtained by Breuer et al. ([11]) for a rainforest ecosystem (Q10 = 3.60, temperature range: 14-24 °C) and the values derived from the data set by Ingwersen et al. ([22]) for a temperate forest ecosystem. In this context, Q10 values derived under field conditions merely indicate the “apparent” temperature sensitivity ([50]), because other factors such as WFPS or N availability are not constant in time and may superimpose with the temperature response. This may explain the quite high Q10 values, which are usually not found in lab incubation experiments.

Soil moisture conditions can markedly impact the microbial processes and ecological interactions involved in nutrient cycling, such as soil C and N turnover rates ([6], [8], [15], [29]). In this study, both soil respiration and gross nitrification rates were positively correlated with soil WFPS (Fig. 2b and Fig. 3b). Some authors reported that soil water content has significant positive effects on soil respiration rates ([36], [44]). In addition, Bengtson et al. ([6]) and Chen et al. ([15]) found that both respiration and gross nitrification rates in forest ecosystems increase with increasing moisture under field conditions. Our study demonstrates that those results are applicable under field conditions in subtropical plantations.

Soil C and N turnover are controlled by microbial processes. In general, a warm and humid season is expected to be more favorable for mineralization, whereas the dry season is typically more favorable for immobilization. We found lowest MBC, MBN, total PLFAs, fungal PLFAs and bacterial PLFAs during the wet season, when temperature and soil moisture conditions were favorable for the microbial community (Tab. 3). During the period of the lowest MB, however, we observed the highest soil respiration and gross nitrification rates (Tab. 3). This finding agrees very well with the results of Maithani et al. ([33]) and Arunachalam et al. ([1]). They found that, in regrowth of a disturbed subtropical humid forest in north-east India, the periods of high mineralization coincided with minimum MB, whereas periods of immobilization corresponded with times of highest MB. In the subtropical forest of Meghalaya, India, Das et al. ([16]) also reported highest N-mineralization rates in the rainy season, while MBN was low in the rainy season and high in the dry winter season. As discussed by Maithani et al. ([33]) and Barbhuiya et al. ([3]), lower MB values during the rainy season, when temperature and soil moisture conditions were favorable for the microbes, indicated a period of rapid mineralization in soil. Sarathchandra et al. ([46]) and Singh et al. ([48]) reported that the relatively greater nutrient demand by plants during the wet season (the peak vegetative growth period) limited the availability of nutrients to soil microbes and thereby reduced their immobilization in MB. Moreover, when soil dries out during the dry season, substrate supply might become limiting. Then, microbes may experience resource limitation that can slow down biogeochemical processes and force microbes into a dormant state ([51], [47]). During the dry season, low water content can inhibit microbial activity by lowering intracellular water potential. This causes microbes to acclimate to decreasing water potentials by altering their allocation of resources ([47]). We therefore expect that microorganisms use most of the available resources to synthesize biomass during the transition between wet and dry season. Chen et al. ([14]) also found that a high level of MBC and MBN in late winter in a hoop pine plantation coincided with low temperatures and low microbial activity.

Soil MB and microbial community composition have been shown to affect soil C and N cycling ([9], [63], [29]). Soil in the E. fordii plantation had higher concentrations of SOC, TN, inorganic N and MBN than soil in the two other plantations (Tab. 1 and Tab. 3). Moreover, the highest C and N turnover rates coincided with the lowest fungal biomass and highest N availability (Tab. 1 and Tab. 3). This agrees with other studies that found fungal biomass to be negatively correlated with soil fertility and N availability ([17], [20], [9]). This may be caused by microbial communities affecting C and N processes or, more likely, by N availability affecting the microbial communities ([17]).

Conclusions 

The present study revealed no significant differences in soil respiration and gross nitrification among the three plantations, but seasonal variation in the C and N processes was highly significant. The seasonal variation of soil respiration and gross nitrification was mostly controlled by environmental factors such as soil temperature and soil water content. The role of the microbial community in this context was less clear, while respiration and nitrification were related to MBN and fungal biomass. Moreover, MB and total PLFA content were negatively correlated with C and N turnover, showing that these two measures alone are poor indicators for microbial activity in soils that experience environmental stress such as drought.

Abbreviations 

The following abbreviations were used throughout the paper:

  1. BaPS: barometric process separation;
  2. PLFA: phospholipid fatty acid;
  3. MB: microbial biomass;
  4. SOC: soil organic carbon;
  5. TN: total nitrogen;
  6. MBC: microbial biomass carbon;
  7. MBN: microbial biomass nitrogen;
  8. T5: soil temperature at 5 cm soil depth;
  9. F/B: ratio of fungal to bacterial biomass;
  10. RQ: respiration quotient;
  11. WFPS: water-filled pore space;
  12. Q10: temperature sensitivity;
  13. SDW: soil dry weight.

Acknowledgements 

We are grateful to Lihua Lu, Haolong Yu and Angang Ming for their help with field sampling. This study was funded by the Project in the National Science & Technology Pillar Program during the Twelfth Five-year Plan Period (No. 2012BAD22B0102), National Scientific Foundation of China (31290223) and the Project of the Special Program on Carbon of the Chinese Academy of Sciences (No. XDA05060100).

References

(1)
Arunachalam A, Maithani K, Pandey HN, Tripathi RS (1998). Leaf litter decomposition and nutrient mineralization patterns in regrowing stands of a humid subtropical forest after tree cutting. Forest Ecology and Management 109: 151-161.
::CrossRef::Google Scholar::
(2)
Bååth E, Anderson TH (2003). Comparison of soil fungal/bacterial ratios in a pH gradient using physiological and PLFA-based techniques. Soil Biology and Biochemistry 35: 955-963.
::CrossRef::Google Scholar::
(3)
Barbhuiya AR, Arunachalam A, Pandey HN, Arunachalam K, Khan ML, Nath PC (2004). Dynamics of soil microbial biomass C, N and P in disturbed and undisturbed stands of a tropical wet-evergreen forest. European Journal of Soil Biology 40: 113-121.
::CrossRef::Google Scholar::
(4)
Bardgett RD, Hobbs PJ (1996). Changes in soil fungal: bacterial biomass ratios following reductions in the intensity of management of an upland grassland. Biology and Fertility of Soils 22: 261-264.
::CrossRef::Google Scholar::
(5)
Bauhus J, Paré D, Coté L (1998). Effects of tree species, stand age and soil type on soil microbial biomass and its activity in a southern boreal forest. Soil Biology and Biochemistry 30: 1077-1089.
::CrossRef::Google Scholar::
(6)
Bengtson P, Falkengren-Grerup U, Bengtsson G (2005). Relieving substrate limitation-soil moisture and temperature determine gross N transformation rates. Oikos 111: 81-90.
::CrossRef::Google Scholar::
(7)
Binkley D, Giardina C (1998). Why do tree species affect soils? The warp and woof of tree-soil interactions. Biogeochemistry 42: 89-106.
::CrossRef::Google Scholar::
(8)
Borken W, Matzner E (2009). Reappraisal of drying and wetting effects on C and N mineralization and fluxes in soils. Global Change Biology 15: 808-824.
::CrossRef::Google Scholar::
(9)
Boyle SA, Yarwood RR, Bottomley PJ, Myrold DD (2008). Bacterial and fungal contributions to soil nitrogen cycling under Douglas fir and red alder at two sites in Oregon. Soil Biology and Biochemistry 40: 443-451.
::CrossRef::Google Scholar::
(10)
Bossio DM, Scow KM (1998). Impacts of carbon and flooding on soil microbial communities: phospolipid fatty acid profiles and substrate utilization patterns. Microbial Ecology 35: 265-278.
::CrossRef::Google Scholar::
(11)
Breuer L, Kiese R, Butterbach-Bahl K (2002). Temperature and moisture effects on nitrification rates in tropical rain-forest soils. Soil Science Society of America Journal 66: 834-844.
::CrossRef::Google Scholar::
(12)
Brüggemann N, Rosenkranz P, Papen H, Pilegaard K, Butterbach-Bahl K (2005). Pure stands of temperate forest tree species modify soil respiration and N turnover. Biogeosciences Discussions 2: 303-331.
::CrossRef::Google Scholar::
(13)
Burton J, Chen CR, Xu ZH, Ghadiri H (2007). Gross nitrogen transformations in adjacent native and plantation forests of subtropical Australia. Soil Biology and Biochemistry 39: 426-433.
::CrossRef::Google Scholar::
(14)
Chen CR, Xu ZH, Blumfield TJ, Hughes JM (2003). Soil microbial biomass during the early establishment of hoop pine plantation: seasonal variation and impacts of site preparation. Forest Ecology and Management 186: 213-225.
::CrossRef::Google Scholar::
(15)
Chen YT, Borken W, Stange CF, Matzner E (2011). Effects of decreasing water potential on gross ammonification and nitrification in an acid coniferous forest soil. Soil Biology and Biochemistry 43: 333-338.
::CrossRef::Google Scholar::
(16)
Das AK, Boral L, Tripathi RS, Pandey HN (1997). Nitrogen mineralisation and microbial biomass-N in a subtropical humid forest of Meghalaya, India. Soil Biology and Biochemistry 29: 1609-1612.
::CrossRef::Google Scholar::
(17)
Grayston SJ, Prescott CE (2005). Microbial communities in forest floors under four tree species in coastal British Columbia. Soil Biology and Biochemistry 37: 1157-1167.
::CrossRef::Google Scholar::
(18)
Grenon F, Bradley RL, Titus BD (2004). Temperature sensitivity of mineral N transformation rates, and heterotrophic nitrification: possible factors controlling the post-disturbance mineral N flush in forest floors. Soil Biology and Biochemistry 36: 1465-1474.
::CrossRef::Google Scholar::
(19)
Hackl E, Pfeffer M, Donat C, Bachmann G, Zechmeister-Boltenstern S (2005). Composition of the microbial communities in the mineral soil under different types of natural forest. Soil Biology and Biochemistry 37: 661-671.
::CrossRef::Google Scholar::
(20)
Högberg MN, Högberg P, Myrold DD (2007). Is microbial community composition in boreal forest soils determined by pH, C-to-N ratio, the trees, or all three? Oecologia 150: 590-601.
::CrossRef::Google Scholar::
(21)
Huang XM, Liu SR, Wang H, Hu ZD, Li ZG, You YM (2014). Changes of soil microbial biomass carbon and community composition through mixing nitrogen-fixing species with Eucalyptus urophylla in subtropical China. Soil Biology and Biochemistry 73: 42-48.
::CrossRef::Google Scholar::
(22)
Ingwersen J, Butterbach-Bahl K, Gasche R, Richter O, Papen H (1999). Barometric process separation: new method for quantifying nitrification, denitrification, and nitrous oxide sources in soils. Soil Science Society of America Journal 63: 117-128.
::CrossRef::Google Scholar::
(23)
Ingwersen J, Schwarz U, Stange CF, Ju XT, Streck T (2008). Shortcomings in the commercialized barometric process separation measuring system. Soil Science Society of America Journal 72: 135-142.
::CrossRef::Google Scholar::
(24)
Janssens IA, Lankreijer H, Matteucci G, Kowalski AS, Buchmann N, Epron D, Pilegaard K, Kutsch W, Longdoz B, Grunwald T, Montagnani L, Dore S, Rebmann C, Moors EJ, Grelle A, Rannik U, Morgenstern K, Oltchev S, Clement R, Gudmundsson J, Minerbi S, Berbigier P, Ibrom A, Moncrieff J, Aubinet M, Bernhofer C, Jensen NO, Vesala T, Granier A, Schulze ED, Lindroth A, Dolman AJ, Jarvis PG, Ceulemans R, Valentini R (2001). Productivity overshadows temperature in determining soil and ecosystem respiration across European forests. Global Change Biology 7: 269-278.
::CrossRef::Google Scholar::
(25)
Kiese R, Hewett B, Butterbach-Bahl K (2008). Seasonal dynamic of gross nitrification and N2O emission at two tropical rainforest sites in Queensland, Australia. Plant and Soil 309: 105-117.
::CrossRef::Google Scholar::
(26)
Kiese R, Papen H, Zumbusch E, Butterbach-Bahl K (2002). Nitrification activity in tropical rain forest soils of the Coastal Lowlands and Atherton Tablelands, Queensland, Australia. Journal of Plant Nutrition and Soil Science 165: 682-685.
::CrossRef::Google Scholar::
(27)
Liang RL, Wen HH (1992). Application of fertilizers in Pinus massoniana plantations in Daqingshan, Guangxi Province. Forest Research 5: 138-142.
::Online::Google Scholar::
(28)
Liu GS, Jiang NH, Zhang LD, Liu ZL (1996). Soil physical and chemical analysis and description of soil profiles. Standards Press of China, Beijing, China, pp. 31-32. [in Chinese]
::Google Scholar::
(29)
Lu XY, Yan Y, Fan JH, Wang XD (2012). Gross nitrification and denitrification in Alpine Grassland Ecosystems on the Tibetan Plateau. Arctic, Antarctic, and Alpine Research 44: 188-196.
::CrossRef::Google Scholar::
(30)
Luan JW (2010). Temporal and spatial variations of soil respiration and its controlling factors in warm-temperate Oak (Quercus acutidentata) forests. Dissertation for the Degree, Chinese Academy of Forestry, Beijing, China, pp. 62. [in Chinese]
::Google Scholar::
(31)
Luan JW, Liu SR, Wang JX, Zhu XL, Shi ZM (2011). Rhizospheric and heterotrophic respiration of a warm-temperate oak chronosequence in China. Soil Biology and Biochemistry 43: 503-512.
::CrossRef::Google Scholar::
(32)
Luan JW, Liu SR, Zhu XL, Wang JX, Liu K (2012). Roles of biotic and abiotic variables in determining spatial variation of soil respiration in secondary oak and planted pine forests. Soil Biology and Biochemistry 44: 143-150.
::CrossRef::Google Scholar::
(33)
Maithani K, Tripathi RS, Arunachalam A, Pandey HN (1996). Seasonal dynamics of microbial biomass C, N and P during regrowth of a disturbed subtropical humid forest in north-east India. Applied Soil Ecology 4: 31-37.
::CrossRef::Google Scholar::
(34)
Matejek B, Huber C, Dannenmann M, Kohlpaintner M, Gasche R, Göttlein A, Papen H (2010). Microbial nitrogen-turnover processes within the soil profile of a nitrogen-saturated spruce forest and their relation to the small-scale pattern of seepage-water nitrate. Journal of Plant Nutrition and Soil Science 173: 224-236.
::CrossRef::Google Scholar::
(35)
Matejek B, Kohlpaintner M, Gasche R, Huber C, Dannenmann M, Papen H (2008). The small-scale pattern of seepage water nitrate concentration in an N saturated spruce forest is regulated by net N mineralization in the organic layer. Plant and Soil 310: 167-179.
::CrossRef::Google Scholar::
(36)
Miao FQ, Wang JS, Sun JC, Kang FF, Zhao XH, He ZS (2010). Conversion rate of soil carbon and nitrogen in natural Pinus tabulaeformis forest on the Taiyue Mountains, China. Chinese Journal of Applied and Environmental Biology 16: 519-522.
::Online::Google Scholar::
(37)
Müller C, Abbasi MK, Kammann C, Clough TJ, Sherlock RR, Stevens RJ, Jäger HJ (2004). Soil respiratory quotient determined via barometric process separation combined with nitrogen-15 labeling. Soil Science Society of America Journal 68: 1610-1615.
::CrossRef::Google Scholar::
(38)
Munson AD, Timmer VR (1995). Soil nitrogen dynamics and nutrition of pine following silvicultural treatments in boreal and Great Lakes-St. Lawrence plantations. Forest Ecology and Management 76: 169-179.
::CrossRef::Google Scholar::
(39)
Neill C, Piccolo MC, Melillo JM, Steudler PA, Cerri CC (1999). Nitrogen dynamics in Amazon forest and pasture soils measured by 15N pool dilution. Soil Biology and Biochemistry 31: 567-572.
::CrossRef::Google Scholar::
(40)
Paavolainen L, Smolander A (1998). Nitrification and denitrification in soil from a clear-cut Norway spruce (Picea abies) stand. Soil Biology and Biochemistry 30: 775-781.
::CrossRef::Google Scholar::
(41)
Palmroth S, Maier CA, McCarthy HR, Oishi AC, Kim HS, Johnsen KH, Katul GG, Oren R (2005). Contrasting responses to drought of forest floor CO2 efflux in a loblolly pine plantation and a nearby Oak-Hickory forest. Global Change Biology 11: 421-434.
::CrossRef::Google Scholar::
(42)
Patra AK, Abbadie L, Clays-Josserand A, Degrange V, Grayston SJ, Guillaumaud N, Loiseau P, Louault F, Mahmood S, Nazaret S, Phillippot L, Poly F, Prosser JI, Le Roux X (2006). Effects of management regime and plant species on the enzyme activity and genetic structure of N-fixing, denitrifying and nitrifying bacterial communities in grassland soils. Environmental Microbiology 8: 1005-1016.
::CrossRef::Google Scholar::
(43)
Rosenkranz P, Brüggemann N, Papen H, Xu Z, Horváth L, Butterbach-Bahl K (2006). Soil N and C trace gas fluxes and microbial soil N turnover in a sessile oak (Quercus petraea (Matt.) Liebl.) forest in Hungary. Plant and Soil 286: 301-322.
::CrossRef::Google Scholar::
(44)
Rosenkranz P, Dannenmann M, Brüggemann N, Papen H, Berger U, Zumbusch E, Butterbach-Bahl K (2010). Gross rates of ammonification and nitrification at a nitrogen-saturated spruce (Picea abies (L.) Karst.) stand in southern Germany. European Journal of Soil Science 61: 745-758.
::CrossRef::Google Scholar::
(45)
Ryan MG, Law BE (2005). Interpreting, measuring, and modeling soil respiration. Biogeochemistry 73: 3-27.
::CrossRef::Google Scholar::
(46)
Sarathchandra SU, Perrott KW, Upsdell MP (1984). Microbiological and biochemical characteristics of a range of New Zealand soils under established pasture. Soil Biology and Biochemistry 16: 177-183.
::CrossRef::Google Scholar::
(47)
Schimel J, Balser TC, Wallenstein M (2007). Microbial stress-response physiology and its implications for ecosystem function. Ecology 88: 1386-1394.
::CrossRef::Google Scholar::
(48)
Singh RS, Srivastava SC, Raghubanshi AS, Singh JS, Singh SP (1991). Microbial C, N and P in dry tropical savanna: effects of burning and grazing. Journal of Applied Ecology 28: 869-878.
::CrossRef::Google Scholar::
(49)
Staelens J, Rütting T, Huygens D, Schrijver AD, Müller C, Verheyen K, Boeckx P (2012). In situ gross nitrogen transformations differ between temperate deciduous and coniferous forest soils. Biogeochemistry 108: 259-277.
::CrossRef::Google Scholar::
(50)
Stange CF, Neue H-U (2009). Measuring and modelling seasonal variation of gross nitrification rates in response to long-term fertilisation. Biogeosciences 6: 2181-2192.
::CrossRef::Google Scholar::
(51)
Stark JM, Firestone MK (1995). Mechanisms for soil moisture effects on activity of nitrifying bacteria. Applied and Environmental Microbiology 61: 218-221.
::Online::Google Scholar::
(52)
Stark JM, Hart SC (1997). High rates of nitrification and nitrate turnover in undisturbed coniferous forests. Nature 385: 61-64.
::CrossRef::Google Scholar::
(53)
Ste-Marie C, Houle D (2006). Forest floor gross and net nitrogen mineralization in three forest types in Quebec, Canada. Soil Biology and Biochemistry 38: 2135-2143.
::CrossRef::Google Scholar::
(54)
Tate KR, Ross DJ, Feltham CW (1988). A direct extraction method to estimate soil microbial C: effects of experimental variables and some different calibration procedures. Soil Biology and Biochemistry 20: 329-335.
::CrossRef::Google Scholar::
(55)
Templer P, Findlay S, Lovett G (2003). Soil microbial biomass and nitrogen transformations among five tree species of the Catskill Mountains, New York, USA. Soil Biology and Biochemistry 35: 607-613.
::CrossRef::Google Scholar::
(56)
Torbert HA, Wood CW (1992). Effects of soil compaction and water-filled pore space on soil microbial activity and N losses. Communications in Soil Science and Plant Analysis 23: 1321-1331.
::CrossRef::Google Scholar::
(57)
Tunlid A, Hoitink HAJ, Low C, White DC (1989). Characterization of bacteria that suppress rhizoctonia damping-off in bark compost media by analysis of fatty acid biomarkers. Applied and Environmental Microbiology 55: 1368-1374.
::Online::Google Scholar::
(58)
Vance ED, Brookes PC, Jenkinson DS (1987). An extraction method for measuring soil microbial biomass C. Soil Biology and Biochemistry 19: 703-707.
::CrossRef::Google Scholar::
(59)
Wang FM, Li ZA, Xia HP, Zou B, Li NY, Liu J, Zhu WX (2010a). Effects of nitrogen-fixing and non-nitrogen-fixing tree species on soil properties and nitrogen transformation during forest restoration in southern China. Soil Science and Plant Nutrition 56: 297-306.
::CrossRef::Google Scholar::
(60)
Wang H, Liu SR, Mo JM, Wang JX, Makeschin F, Wolff M (2010b). Soil organic carbon stock and chemical composition in four plantations of indigenous tree species in subtropical China. Ecological Research 25: 1071-1079.
::CrossRef::Google Scholar::
(61)
Wang WX, Shi ZM, Luo D, Liu SR, Lu LH, Ming AG, Yu HL (2013). Carbon and nitrogen storage under different plantations in subtropical south China. Acta Ecologica Sinica 3: 925-933. [in Chinese]
::CrossRef::Google Scholar::
(62)
Westbrook CJ, Devito KJ, Allan CJ (2006). Soil N cycling in harvested and pristine boreal forests and peatlands. Forest Ecology and Management 234: 227-237.
::CrossRef::Google Scholar::
(63)
Yin HJ, Chen Z, Liu Q (2012). Effects of experimental warming on soil N transformations of two coniferous species, Eastern Tibetan Plateau, China. Soil Biology and Biochemistry 50: 77-84.
::CrossRef::Google Scholar::
(64)
Zhang XL, Wang QB, Li LH, Han XG (2008). Seasonal variations in nitrogen mineralization under three land use types in a grassland landscape. Acta Oecologica 34: 322-330.
::CrossRef::Google Scholar::

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Wang W, Cheng R, Shi Z, Ingwersen J, Luo D, Liu S (2016).
Seasonal dynamics of soil respiration and nitrification in three subtropical plantations in southern China
iForest - Biogeosciences and Forestry 9: 813-821. - doi: 10.3832/ifor1828-009
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Paper ID# ifor1828-009
Title Seasonal dynamics of soil respiration and nitrification in three subtropical plantations in southern China
Authors Wang W, Cheng R, Shi Z, Ingwersen J, Luo D, Liu S
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