Temporal changes in soil bacterial and archaeal communities with different fertilizers in tea orchards#

Temporal changes in soil bacterial and archaeal communities with different fertilizers in tea orchards # # 不同肥料处理下茶园土壤细菌和古菌群落的时间变化研究 1 first-author 1 1 corresp ‡Corresponding Author ‡Corresponding Author †E-mail:jpyang@zju.edu.cn †E-mail:jpyang@zju.edu.cn jpyang@zju.edu.cn 1 1 2 Institute of Environmental Protection, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China Institute of Environmental Protection, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China College of Life and Environmental Sciences, Hangzhou Normal University, Hangzhou 310036, China College of Life and Environmental Sciences, Hangzhou Normal University, Hangzhou 310036, China It is important to understand the effects of temporal changes in microbial communities in the acidic soils of tea orchards with different fertilizers. A field experiment involving organic fertilizer (OF), chemical fertilizer (CF), and unfertilized control (CK) treatments was arranged to analyze the temporal changes in the bacterial and archaeal communities at bimonthly intervals based on the 16S ribosomal RNA (rRNA) gene using terminal restriction fragment length polymorphism (T-RFLP) profiling. The abundances of total bacteria, total archaea, and selected functional genes (bacterial and archaeal amoA , bacterial narG , nirK , nirS , and nosZ ) were determined by quantitative polymerase chain reaction (qPCR). The results indicate that the structures of bacterial and archaeal communities varied significantly with time and fertilization based on changes in the relative abundance of dominant T-RFs. The abundancy of the detected genes changed with time. The total bacteria, total archaea, and archaeal amoA were less abundant in July. The bacterial amoA and denitrifying genes were less abundant in September, except the nirK gene. The OF treatment increased the abundance of the observed genes, while the CF treatment had little influence on them. The soil temperature significantly affected the bacterial and archaeal community structures. The soil moisture was significantly correlated with the abundance of denitrifying genes. Of the soil chemical properties, soil organic carbon was the most important factor and was significantly correlated with the abundance of the detected genes, except the nirK gene. Overall, this study demonstrated the effects of both temporal alteration and organic fertilizer on the structures of microbial communities and the abundance of genes involved in the nitrogen cycle. 研究目的 2 研究化学肥料和有机肥处理条件下，茶园酸性土壤细菌和古菌群落结构，以及氮素转化相关功能酶基因丰度的时间变化规律。 创新要点 2 研究肥料、土壤温度及土壤含水量对茶园酸性土壤细菌和古菌群落结构，以及氮素转化相关功能酶基因丰度的影响。 研究方法 2 应用末端限制性片段长度多态性（T-RFLP）技术分析茶园酸性土壤中细菌和古菌群落结构随时间的变化规律，应用荧光定量聚合酶链式反应（PCR）技术，研究茶园酸性土壤细菌、古菌、硝化作用功能酶基因（细菌和古菌 amoA 基因）和细菌反硝化作用功能酶基因（ narG 、 nirK 、 nirS 和 nosZ 基因）丰度的时间变化规律。 重要结论 2 茶园土壤细菌和古菌群落结构受到肥料的影响，并随着取样时间有显著的变化。细菌、古菌和古菌的 amoA 基因的丰度在7月份最小，而细菌的 amoA 基因和反硝化作用功能酶基因（除 nirK 基因）的丰度在9月份最小。有机肥处理增加了细菌、古菌和氮素转化相关功能酶基因的丰度，但化学肥料的施用对菌群及功能酶基因丰度的影响较小。土壤温度显著影响了土壤细菌和古菌的群落结构。土壤含水量与细菌反硝化作用功能酶基因有显著的相关性。土壤有机碳含量与细菌、古菌及功能酶基因丰度之间有显著的相关性。 Bacterial and archaeal communities Fertilizer Soil Temporal changes Tea orchard Functional genes 茶园 时间变化 肥料 细菌和古菌群落 氮素转化相关功能酶基因 Introduction 1 Soil microbial communities can affect the crop quality, the sustainability of agro-ecosystems (Stark et al., 2008 2008 ), and the biogeochemical cycle of soil nutrients (Buckley and Schmidt, 2003 2003 ). The soil nitrogen (N) cycle contains two important processes: nitrification and denitrification. The oxidation of ammonia, the first and rate-limiting step of nitrification, is catalyzed by ammonia monooxygenase. This enzyme is encoded by the amoA gene from ammonia-oxidizing bacteria (AOB) and ammonia-oxidizing archaea (AOA), which may occupy different ecological niches (Prosser and Nicol, 2012 2012 ). Denitrification, a four-step reducing process that is catalyzed by key enzymes encoded by the narG , nirK , nirS , and nosZ genes (Zumft, 1997 1997 ), plays an important role in the reduction of nitrate to gaseous nitrogen. Studies of the nitrifiers and denitrifiers are commonly focused on functional genes, such as amoA , narG , nirK , and nirS (Szukics et al., 2010 2010 ; Di et al., 2014 2014 ). The application of chemical fertilizer (CF) can increase the crop yield but changes the soil properties and results in N loss from agricultural systems (Galloway et al., 2003 2003 ). CF increases the microbial biomass and changes the bacterial community structure (Peacock et al., 2001 2001 ); however, it does not significantly increase the bacterial abundance (Chen et al., 2012 2012 ) or decrease the bacterial and archaeal abundances in long-term experiments (Hallin et al., 2009 2009 ; Chan et al., 2013 2013 ). Organic fertilizer (OF), an alternative to CF, mitigates N pollution from agriculture ecosystems. The organic matter amendment not only increases the microbial biomass (Peacock et al., 2001 2001 ) and total bacterial and archaeal abundance (Hallin et al., 2009 2009 ; Chan et al., 2013 2013 ), but also changes the structure of bacterial community through the increased soil organic carbon (SOC) content. Shen et al. ( 2008 2008 ) and Ai et al. ( 2013 2013 ) found that chemical N fertilizer changed the AOB community structure and increased the soil potential nitrification activity (PNA) and AOB abundance, but He et al. ( 2007 2007 ) reported that soil PNA and AOB abundances decreased with the application of a chemical N fertilizer. The effect of chemical N fertilizer on the abundance of AOA varies due to the soil pH (He et al., 2007 2007 ; Shen et al., 2008 2008 ; Ai et al., 2013 2013 ). Manure amendment significantly increased the AOB abundance in a paddy field with long-term fertilization (Wang et al., 2014 2014 ) but not in other systems (Wessén et al., 2010 2010 ; Ai et al., 2013 2013 ). For AOA, manure increased the AOA abundance and changed the community structure (Wessén et al., 2010 2010 ; Ai et al., 2013 2013 ). For denitrifiers, the influence of chemical N fertilizer and OF has been well studied but appears to have incongruent effects on denitrifier community composition (Wolsing and Priemé, 2004 2004 ; Enwall et al., 2005 2005 ; Clark et al., 2012 2012 ). In a long-term experiment, ammonium fertilizer significantly decreased the abundance of denitrifiers (Hallin et al., 2009 2009 ). Manure amendment increased the abundance of denitrifying genes both at the DNA and RNA levels (Hallin et al., 2009 2009 ; Clark et al., 2012 2012 ; Saunders et al., 2012 2012 ). It is important to study the temporal changes in soil microbial communities because temporal analyses may help to comprehensively explain the soil function and provide insight into the environmental niches that are inhabited by diverse soil microbes (Lauber et al., 2013 2013 ). The abundances and community structures of bacteria and archaea (He et al., 2007 2007 ; Rasche et al., 2011 2011 ), including ammonia-oxidizing ones (He et al., 2007 2007 ; Szukics et al., 2010 2010 ), vary temporally due to changes in the soil properties. For denitrifiers, previous studies have demonstrated that the temporal effect on the abundance of denitrifying genes varies among different genes (Wolsing and Priemé, 2004 2004 ; Dandie et al., 2008 2008 ). A temporal shift in the denitrifier community composition has also been reported (Wolsing and Priemé, 2004 2004 ; Dandie et al., 2008 2008 ; Szukics et al., 2010 2010 ). Tea ( Camellia sinensis ) is an important cash crop in southern China. The soil pH in tea orchards is relatively low because of the tea plantations and high-level applications of chemical N fertilizers, especially urea (Yao et al., 2011 2011 ). However, the study of the temporal changes in the soil microbial communities that are related to OF treatment is relatively rare in tea orchards. Therefore, this study focused on the temporal changes of the bacterial and archaeal communities and functional groups as related to nitrification and denitrification under different types of fertilizer treatments. The major hypotheses were (1) that fertilizer amendments would influence the structures of bacterial and archaeal communities and the abundances of the functional groups that are involved in nitrification and denitrification in tea orchards, and (2) that the temporal variability in microbial community structures and the abundance of detected genes may be related to soil climate changes, such as soil temperature and moisture variation. In this study, the abundances and community structures of total bacteria and total archaea, as well as the abundances of denitrifying ( narG , nirS , nirK , and nosZ ) and nitrifying (AOA and AOB) functional groups were investigated. Materials and methods 1 Field site, experimental design, and soil sampling 2 The present study was conducted in the Jingshan tea production field (119°53′ E, 30°23′ N), Hangzhou, China. This area has a subtropical monsoon climate with an annual average air temperature of 17 °C and annual average rainfall of 1400 mm. The soil of the tea field is red clay, and the topsoil (0–10 cm) has a pH (H 2 O) value of 3.72, SOC of 35.80 g/kg, and total nitrogen (TN) level of 3.16 g/kg. The tea plants ( C. sinensis L.) in the field were approximately 40 years old. The farmer applied CF, including urea and calcium superphosphate, three times (in March, June, and October) per year before the study was conducted to improve the tea yield. The field experiment began in March 2012 with nine experimental plots that were 5 m wide and 20 m long. The plots were separated by a concrete wall that was 35 cm high aboveground and 30 cm deep underground to prevent water flow between the plots. The applied fertilizer treatments in this study included unfertilized control (CK), OF, and CF. The properties and application details of the fertilizers are shown in Table 1 1 . To be consistent with the tea cultivation by the native farmers, the fertilizer was applied in three split applications in 2012. The experimental design was a completely randomized design, and each fertilizer treatment had three replicates. The fertilizer for each plot was applied equally to the rows between the tea plants. CF: chemical fertilizer; OF: organic fertilizer; TN: total nitrogen; AP: available phosphorus 2 Treatment 2 Fertilizer 2 TN (g/kg) 2 AP (g/kg) 3 Application of fertilizer Mar. 21 June 14 Oct. 26 CF Urea and calcium superphosphate 464.0 126.0 120 kg N/ha, 40 kg P/ha 90 kg N/ha, 30 kg P/ha 90 kg N/ha, 30 kg P/ha OF Commercially made from manure of pig, chicken, etc. 31.8 11.0 120 kg N/ha, 40 kg P/ha 90 kg N/ha, 30 kg P/ha 90 kg N/ha, 30 kg P/ha Soil sampling was conducted every two months from May 17, 2012 to March 17, 2013. The soil sample of each plot was composed of 12 soil cores that were collected along zigzag paths (zigzag sampling) from the surface layer (0–10 cm). All of the soil cores were sampled from fertilized rows. A total of 12 soil cores from each plot were delivered on ice to the laboratory and were then pooled and sieved through 2- or 5-mm mesh (wet soils). Visible pieces of plant material and soil-dwelling animals were removed. To measure the soil ammonium and nitrate levels and perform a molecular analysis, the soil subsamples were stored at −20 °C. The soils were air-dried before the measurements of SOC, TN, and pH. Soil property analysis 2 The soil moisture content was determined by oven-drying 10 g of soil at 105 °C for 24 h, and the results are presented based on the soil weight loss. The soil temperature was measured using a digital thermometer at a depth of 10 cm. The soil pH was determined by a digital pH meter after extracting with water (1 g soil extracted with 2.5 ml water). The SOC and TN were analyzed by oxidation with dichromate (Nelson and Sommers, 1982 1982 ) and Kjeldahl determination (VELP Scientifica, Italy), respectively. The ammonium and nitrate contents were determined using the indo-phenol-blue colorimetric method (Sun, 2007 2007 ) and dual-wavelength ultraviolet spectrophotometry (Norman et al., 1985 1985 ), respectively, after extracting with 2 mol/L KCl solution (1 g soil extracted with 5 ml KCl solution). DNA extraction and quantitative polymerase chain reaction (qPCR) analysis 2 DNA was extracted from 0.5 g of soil using the FastDNA Spin Kit for Soil (MP Biomedicals, USA). The abundances of the total bacteria, total archaea, bacterial and archaeal amoA genes, and the bacterial narG , nirS , nirK , and nosZ genes were determined by qPCR with an ABI 7500 instrument (Applied Biosystems, USA). The PCR procedures and primers are shown in Table S1 S1 . Each reaction was performed in a volume of 25 μl containing 0.4 μl of each primer (10 μmol/L), 0.5 μl of ROXII (TaKaRa, Japan), 12.5 μl of 2×SYGB buffer (TaKaRa), 0.625 μl of DMSO, 0.875 μl of 3% bovine serum albumin (BSA), and 10 ng of template. For the standard curves, the amplicons of the detected genes were purified using the Cycle-Pure Kit (Omega Bio-Tek, USA), ligated into the vector pMD18-T (TaKaRa) and then transformed into Escherichia coli TOP10 competent cells. The plasmids were extracted using the Plasmid Mini Kit (Omega Bio-Tek) and checked using the basic local alignment search tool (BLAST). A NanoDrop 2000 (Thermo, USA) was used to measure the concentrations of the plasmids. The plasmids were serially diluted to generate the standard curves. Microbial community analysis by terminal restriction fragment length polymorphism (T-RFLP) 2 For T-RFLP, the bacterial and archaeal 16S ribosomal RNA (rRNA) genes were amplified using FAM-labeled forward primers. The PCR was performed in a C1000 Thermal Cycler (Bio-Rad, USA). The primer sets and amplification details of the PCR for T-RFLP are shown in Table S1 S1 . The 50 μl PCR cocktail contained 10 ng of DNA, 0.15 μmol/L of each primer, 0.2 mmol/L dNTPs, 0.5 mg/ml BSA, 4 mmol/L Mg 2+ , 2 U of HS Taq (TaKaRa), and a 1× reaction buffer that was provided with the enzyme. Duplicate amplifications from each sample were mixed and purified using a gel extraction kit (Omega Bio-Tek). The bacterial and archaeal amplicons were digested with Hha I and Alu I (NEB, USA), respectively, at 37 °C for 3 h. A 2-μl aliquot of each digest was mixed with 12 μl of formamide and 0.3 μl of the internal size standard (ROX-500, Applied Biosystems) and detected on an ABI 3730xl genetic analyzer (Applied Biosystems). The fragments were analyzed using the GeneMapper software (Version 4.0, Applied Biosystems). The height of each peak was expressed as the percentage of all of the peak heights in the profile. The peak was included for further analysis if its length was between 50 and 500 bp and its percentage was > 1%. Statistical analysis 2 For the soil properties and qPCR data that were not normally distributed, the data were log-transformed. A repeated-measure analysis of variance (ANOVA) was performed to analyze the effect of fertilizer and sampling time on the soil properties and qPCR data. We compared the significant differences among the fertilizers at each sampling time using Tukey’s test. A correlation analysis was used to examine the relationship between the soil properties and the abundance of phylogenetic and functional markers. Statistical analyses were performed with SPSS V16.0. The T-RFLP data were square-root-transformed prior to analysis. A two-way interactive permutational multivariate ANOVA (PERMANOVA) (Anderson, 2001 2001 ) was conducted to assess the effect of fertilizer and sampling time on the bacterial and archaeal communities. The pairwise comparisons in the PERMANOVA were used to determine whether the bacterial and archaeal communities were altered by fertilizer at each sampling time. The PERMANOVA generated a Monte Carlo asymptotic P -value ( P _MC) when the pairwise comparisons were conducted. The microbial communities significantly differed among the fertilizer treatments if P _MC < 0.05. Nonmetric multidimensional scaling (nmMDS) based on the Bray-Curtis similarity coefficients was used to analyze the relatedness of individual community profiles with all treatments conducted with Primer V5.0 software (PRIMER-E, UK). A redundancy analysis (RDA) with the Monte Carlo permutation test with 999 permutations was performed with Canoco (Version 4.5 for Windows, Biometrics Plant Research International, the Netherlands) to test the influence of the soil properties on the microbial community. Results 1 Soil properties 2 The soil properties varied significantly with the fertilizers and sampling time ( P < 0.01). However, the interaction between the fertilizers and sampling time only significantly influenced the soil ammonium and nitrate levels ( P < 0.01). The soil TN, ammonium, and nitrate levels were higher under the CF treatment, but the soil pH was significantly lower under the CF treatment than under the OF or CK treatment, except on November 18th (Table S2 S2 ). The OF treatment had a higher SOC content. Abundances of bacteria and archaea 2 The abundances of total bacteria and archaea under the OF, CF, and CK treatments changed similarly with respect to the sampling time and was lowest on July 19th (Fig. 1 1 ). The OF treatment had the highest copy numbers of total bacterial and archaeal 16S rRNA genes, ranging from 1.7×10 9 to 4.9×10 9 and 1.3×10 7 to 1.1×10 8 copies per gram of dry soil, respectively (Fig. 1 1 ). A greater bacterial abundance was detected under the CF treatment than under the CK treatment with an exception on July 19th, but a significant difference was only shown on January 15th. No significant difference was detected for the archaeal abundance between the CF and CK treatments. Data are expressed as mean±SD, n =3. The letters above the bars indicate significant differences ( P < 0.05) among the fertilizers for each sampling time. The Y axis is a logarithmic coordinate axis Abundances of the bacterial and archaeal amoA genes 2 Both of the bacterial and archaeal amoA gene copy numbers changed significantly with the sampling time ( P < 0.01). The archaeal amoA gene copy number changed from 3.7×10 5 to 2.3×10 7 copies per gram of dry soil and was lowest on July 19th (Fig. 2a 2a ). The population size of AOB was lower than that of AOA (Fig. 2b 2b ), the lowest AOB abundance was on September 16th, when no significant differences were detected among the three treatments. The OF treatment resulted in the greatest abundance of both the bacterial and archaeal amoA genes. Data are expressed as mean±SD, n =3. The letters above the bars indicate significant differences ( P < 0.05) among the fertilizers for each sampling time. The Y axis is a logarithmic coordinate axis Abundance of denitrifiers 2 All of the detected denitrifying genes were more abundant under the OF treatment, but the effect was not always significant (Fig. 3 3 ). No significant difference was found when comparing the CF treatment with the CK treatment, with the exception of January 15th when the narG gene was more abundant under the CF treatment. The lowest denitrifying gene abundance was determined on September 16th except for that of nirK under the CF and CK treatments. The analysis of variance detected a significant variation of the abundance of denitrifying genes with the sampling time ( P < 0.01). Data are expressed as mean±SD, n =3. The letters above the bars indicate significant differences ( P < 0.05) among the fertilizers for each sampling time. The Y axis is a logarithmic coordinate axis Bacterial and archaeal communities 2 Bacterial community composition profiles showed 93 T-RFs across all of the profiles, of which three dominant fragments (58, 88, and 372 bp) and a further six fragments (63, 74, 211, 279, 294, and 298 bp) comprised > 10% and 5% of the total abundance, respectively. The PERMANOVA analysis indicated a significant effect of the fertilizer and sampling time ( P < 0.01), which was further supported by nmMDS analysis (Fig. S1a S1a ). However, no significant differences were detected between the CF and OF treatments in July, January, or March (Table 2 2 ). The relative abundance of T-RF-58 varied significantly with the fertilizer except on July 19th and September 16th (Fig. 4a 4a ). The relative abundance of T-RF-88 changed significantly among the three treatments from July 19th to March 17th. While the relative abundance of T-RF-372 was significantly higher under the CK treatment on July 19th, this fragment was not detected from September to March. P _MC: Monte Carlo asymptotic P -value 3 Sampling time 6 P _MC value 3 Bacterial community 3 Archaeal community CF vs. OF CF vs. CK OF vs. CK CF vs. OF CF vs. CK OF vs. CK May 17 0.024 0.003 0.030 0.004 0.004 0.014 July 19 0.071 0.029 0.006 0.055 0.015 0.036 Sept. 16 0.027 0.013 0.009 0.005 0.007 0.011 Nov. 18 0.048 0.012 0.005 0.003 0.004 0.012 Jan. 15 0.079 0.002 0.010 0.010 0.003 0.032 Mar. 17 0.077 0.033 0.050 0.002 0.004 0.037 The T-RFs that never contributed more than 5% were summarized as “others” The archaeal community analysis revealed 88 peaks across all of the profiles, with four major peaks (141, 404, 405, and 435 bp) and six peaks (70, 116, 347, 350, 407, and 500 bp) forming > 20% and > 5% of the total abundance, respectively. The nmMDS plot indicated differences among the treatments throughout the entire sampling time with a stress value of 0.18 (Fig. S1b S1b ), which are supported by the PERMANOVA analysis ( P < 0.01). These dominant fragments varied significantly with the treatments. T-RF-404 was significantly more abundant under the CF treatment, while no significant difference was detected between the OF and CK treatments except on November 18th (Fig. 4b 4b ). The OF treatment but not the CF treatment significantly increased the relative abundance of T-RF-141. The relative abundance of T-RF-435 significantly decreased under the CF treatment except on January 15th, while the T-RF-405 fragment was more abundant under the CF treatment except on May 17th. Relationships between soil properties and microbial abundance and microbial community structure 2 The SOC was significantly positively correlated with the abundance of all of the detected genes except for the archaeal amoA and nirK genes (Table 3 3 ). The abundance of AOA correlated negatively with nitrate but positively with the pH and the ratio of SOC to TN (C:N). The abundance of the nirK gene was only positively correlated with nitrate. The abundance of the nirS gene was negatively correlated with TN but positively correlated with the SOC and C:N. The pH was positively correlated with the abundances of total bacteria and the nosZ gene. No significant correlation was found between the soil ammonium content and the abundance of any of the detected genes. The soil temperature was negatively correlated with the abundance of total bacteria and positively correlated with the abundance of the nirK gene. The soil moisture was positively correlated with the abundances of narG , nirK , and nirS . Significant at the level of 0.05 (two-tailed) Significant at the level of 0.01 (two-tailed) C:N: ratio of soil organic carbon (SOC) to total nitrogen (TN) 2 Prokaryotic group 8 Pearson’s linear correlation coefficient Ammonium Nitrate pH SOC TN C:N Temperature Moisture Total bacteria −0.007 −0.296 * * 0.355 * * 0.434 * * 0.114 0.283 * * −0.554 * * −0.024 Total archaea 0.000 −0.237 0.167 0.288 * * −0.017 0.252 0.023 0.162 Archaeal amoA gene −0.210 −0.306 * * 0.446 * * 0.235 −0.171 0.333 * * 0.027 0.005 Bacterial amoA gene 0.067 0.080 0.001 0.275 * * −0.009 0.258 0.074 0.076 Bacterial narG gene 0.153 0.017 −0.018 0.302 * * 0.092 0.181 −0.125 0.318 * * Bacterial nirK gene −0.022 0.277 * * −0.197 0.160 −0.139 0.263 0.645 * * 0.286 * * Bacterial nirS gene −0.098 0.002 0.110 0.295 * * −0.270 * * 0.516 * * 0.241 0.292 * * Bacterial nosZ gene −0.174 −0.190 0.415 * * 0.430 * * −0.181 0.549 * * −0.198 0.113 The relationship between the soil properties and the microbial communities under all of the treatments was analyzed using RDA (Fig. 5 5 ). The length of the arrow in the figure represents the effect of the soil properties on the microbial community distribution, which increased along the arrow’s direction. The bacterial communities were mainly influenced ( P < 0.05) by soil temperature, TN, C:N, ammonium, and moisture. Soil ammonium, temperature, SOC, pH, and C:N significantly ( P < 0.05) affected the archaeal communities. The cumulative variances of the species-environment relationship as explained by the first two axes were 69.60% and 76.70% for the bacterial and archaeal communities, respectively. The first axis explains the largest variation of the bacterial communities ( P < 0.01) and archaeal communities ( P < 0.01). For the bacterial communities, the first axis accounted for 13.40% of the variation in the species data and was strongly correlated with the soil temperature. The archaeal species data were changed mainly by the first axis, which attributed to 22.60% of the variation and was strongly correlated with the soil ammonium content, pH, and C:N. The numbers in the plots represent the sampling time: 1. May 17; 2. July 19; 3. Sept. 16; 4. Nov. 18; 5. Jan. 15; 6. Mar. 17. C:N is the ratio of soil organic carbon to total nitrogen. Only the statistically significant soil properties are shown Discussion 1 Temporal changes in the soil bacterial and archaeal communities under different fertilizer treatments 2 We detected a significant effect of soil temperature on the community structures of the total bacteria and archaea, similar to other researchers (Rasche et al., 2011 2011 ; Lauber et al., 2013 2013 ). Temperature is an important factor in microbial community composition (Hayden et al., 2012 2012 ). The Gram-positive (G + ) bacterial abundance responds negatively to the winter months with lower soil temperatures (Bell et al., 2009 2009 ). Elevated temperatures increase the abundance of G + bacteria, including Firmicutes and Actinobacteria , but decrease the abundance of Gram-nagative (G − ) bacteria (Gray et al., 2011 2011 ; Hayden et al., 2012 2012 ). For archaeal community composition, Bomberg et al. ( 2011 2011 ) found that the crenarchaeotal denaturing gradient gel electrophoresis (DGGE) band numbers decreased with increasing temperatures, while the number of euryarchaeotal DGGE bands increased. Interestingly, no significant differences were detected in the bacterial community structure between the CF and OF treatments three months after the last fertilization, indicating that the dynamics of the soil bacterial community structure was unrelated to the applied materials after a certain duration of fertilization. Such a time-related variation in the bacterial community was also found by Calbrix et al. ( 2007 2007 ) and may be attributed to a seasonal effect. The bacterial and archaeal abundances changed significantly with the sampling time and were lower in the summer than in the winter (Rasche et al., 2011 2011 ; Jung et al., 2012 2012 ). In agreement with He et al. ( 2007 2007 ), the abundance of total bacteria did not change significantly among the three treatments in the summer. Very low soil microbial counts were detected when the soil was sampled following a heavy rainfall (Pandey and Palni, 1996 1996 ). There was a heavy rainfall (as great as 57 mm) at the experimental site on July 17, which might be the reason for the lowest abundance of total bacteria and archaea being detected on July 19. Fertilizer amendments increase the soil nutrient levels, including the soil SOC, TN, and ammonium contents, and thus influence the microbial community structure (Nicol et al., 2003 2003 ; Böhme et al., 2005 2005 ). However, long-term fertilization did not significantly change the bacterial community composition (He et al., 2008 2008 ; Ogilvie et al., 2008 2008 ). Of the tested soil chemical properties, the C:N and ammonium content significantly influenced the bacterial and archaeal community structures. OF directly increased the SOC and pH, changed the soil C:N and supported the growth of specific microorganisms (Marschner et al., 2003 2003 ; Gattinger et al., 2007 2007 ). For example, Marschner et al. ( 2003 2003 ) found that OF increased the ratio of G + to G − bacteria compared to the ratio under inorganic fertilizer. The mechanisms by which inorganic N fertilizer changes the microbial community structure are complicated (Clegg, 2006 2006 ) and possibly include several actions. First, mineral nitrogen, as a substrate for growth or respiration, may affect the microbial community structure through the competition of microbes (Clegg, 2006 2006 ). Second, soil pH, which decreased after ammonium nitrogen fertilization, is an important driver of the microbial community structure (Hansel et al., 2008 2008 ). Third, CFs significantly increase the soil TN, which can influence the soil C:N ratio, thereby affecting the soil microbial community structure. In addition, CF influences the soil C:N ratio by stimulating root exudates and increasing the litter-fall quantity through improving the growth of tea plants. In this study, the bacterial and archaeal abundances were in the same range as those of other studies in acidic soils of other agro-ecosystem (He et al., 2007 2007 ; Kemnitz et al., 2007 2007 ). OF amendment increased the bacterial and archaeal abundances (Gattinger et al., 2007 2007 ; Zhang et al., 2012 2012 ; Chan et al., 2013 2013 ), perhaps due to the enhancement of the SOC content. In addition, organic manure may be beneficial to the proliferation of microbes (Sun et al., 2004 2004 ). However, the CF treatment did not significantly affect the total abundance of bacteria and archaea. Application of CF not only increases the soil C content, which increases the total abundance of bacteria and archaea, but also decreases the soil pH, which decreases the total abundances of bacteria and archaea (He et al., 2007 2007 ). Temporal changes in the abundance of AOA and AOB under different fertilizer treatments 2 Previously, many studies have reported temporal changes in the abundance of AOA and AOB and attributed these changes to variations in the soil moisture and temperature (Rasche et al., 2011 2011 ). However, Andert et al. ( 2011 2011 ) found that the abundance of AOA did not change over a 9-month experiment. In this study, we found variations in the abundances of AOA and AOB with the sampling time that was not correlated with the soil moisture or temperature. Temporal effect is a comprehensive pattern that includes abiotic and biotic factors, such as soil temperature and moisture, rainfall, and tea plant growth. In addition, the soil temperature and moisture affect the plant growth and the transformation of soil nutrients (Gutknecht et al., 2012 2012 ), and therefore influence the abundances of AOA and AOB indirectly. However, this indirect effect cannot always be detected using a correlation analysis. Interestingly, Rasche et al. ( 2011 2011 ) reported that the AOA and AOB abundances were lower in the summer than in the winter, but He et al. ( 2007 2007 ) found a contradicted result. The reason for this inconsistency may be differences in the soil types, soil pH, plant types, and other environmental factors. The abundances of AOA and AOB could be affected by fertilizer amendments. OF, as a source of bioavailable SOC (Ai et al., 2013 2013 ; Chan et al., 2013 2013 ), stimulated the abundances of AOA and AOB (He et al., 2007 2007 ; Shen et al., 2008 2008 ; Ai et al., 2013 2013 ). Compared to the CK treatment, CF decreased the soil pH and AOA abundance, as reported by He et al. ( 2007 2007 ). However, Ai et al. ( 2013 2013 ) found a contradicted result in a calcareous fluvo-aquic soil, possibly because chemical fertilization did not reduce the soil pH in the calcareous soil as the soil pH was significantly correlated with the abundance of AOA. CF treatment significantly enhances AOB abundance in alkaline soils (Shen et al., 2008 2008 ; Ai et al., 2013 2013 ) but not in acidic soils (He et al., 2007 2007 ). In this study, CF increased the AOB abundance due to the increase of SOC, most likely because of the increased litter quantity and the tea root residue. In addition, AOA were more abundant than AOB, indicating that AOA have a greater role in acidic soils (Yao et al., 2011 2011 ; Zhang et al., 2011 2011 ). Temporal changes in the abundance of denitrifiers under different fertilizer treatments 2 Previous studies have demonstrated that the abundance of denitrifying genes fluctuates with the seasons (Dandie et al., 2008 2008 ; Wakelin et al., 2009 2009 ; Rasche et al., 2011 2011 ), but no consistent trend was found (Dandie et al., 2008 2008 ; Rasche et al., 2011 2011 ). We found temporal changes in the abundance of denitrifying genes, such as narG , nirK , and nirS , which may be due to changes in the soil moisture because the soil moisture was significantly correlated with the abundance of denitrifying genes, with the exception of nosZ . The copy numbers of denitrifying genes, such as nirK and nirS , increase with increasing the water content (Szukics et al., 2010 2010 ; Di et al., 2014 2014 ), but nosZ communities exhibit a great stability in response to changes in the soil moisture (Stres et al., 2008 2008 ). Pastorelli et al. ( 2011 2011 ) reported that the season did not affect the nosZ -gene-bearing denitrifiers, but we detected a variation of nosZ with the sampling time. Of the measured soil chemical properties, the soil SOC, pH and C:N were significantly correlated with the copy numbers of the nosZ gene. The temporal effect on the copy numbers of the nosZ gene might be due to the combined function of the soil properties and should be studied in future research. The quantification of the denitrifying genes revealed a lower abundance in tea orchard soil (Hallin et al., 2009 2009 ; Chen et al., 2012 2012 ; Clark et al., 2012 2012 ). The abundances of the denitrifying genes were in the following order: narG > nirK > nirS > nosZ . This phenomenon may be caused by the different gene copies within denitrifier genomes (Chen et al., 2012 2012 ) and the decreased concentration of the denitrifying intermediates in the soil following the reduction order (Yu et al., 2008 2008 ). The greater abundance of nirK than of nirS observed in tea orchard soil was consistent with Clark et al. ( 2012 2012 ), but a contrasting phenomenon was reported in grassland (Čuhel et al., 2010 2010 ). The bacteria carrying nirK genes become more abundant in response to increased N, while the bacteria carrying nirS may have an advantage in low-N soils (Clark et al., 2012 2012 ). In tea orchards, the soil N increased due to the input of a large amount of CF used to enhance the tea yield. The qPCR analysis revealed that the OF increased the abundance of denitrifiers due to the added biomass C. Because most denitrifiers are heterotrophs (Saunders et al., 2012 2012 ), we also found a significant correlation between the SOC as well as the C:N and the number of denitrifying genes. However, Hallin et al. ( 2009 2009 ) reported that an OF treatment alone did not significantly affect the abundance of denitrifiers. In general, no significant effect of CFs was detected. The CF amendments significantly decreased the soil pH, which was a negative factor regarding the copy numbers of denitrifying genes and could counteract the positive effects of higher SOC induced by CFs (Hallin et al., 2009 2009 ). In addition, the effect of CFs on denitrifier abundance is likely to be related to the soil C:N ratio, which was reduced by the N fertilizer amendments, changing the population of denitrifiers (Rösch and Bothe, 2009 2009 ). 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This article does not contain any studies with human or animal subjects performed by any of the authors. 10.1631/jzus.B1400114 Project supported by the Research Fund for the Doctoral Program of Higher Education of China (No. 20130101110127) and the Project of Zhejiang Key Scientific and Technological Innovation Team (No. 2010R50039), China 2014-04-22 2014-10-15 2014-08-31 2022-01-05 Copyright © Zhejiang University and Springer-Verlag Berlin Heidelberg 2014 2014 Journal of Zhejiang University-SCIENCE B (Biomedicine and Biotechnology) 11 15