Journal of Sustainable Forestry
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Characteristics of soil microbial biomass and community composition in Pinus yunnanensis var. Tenuifolia secondary forests Sufang Yu, Guanghui She, Shaoming Ye, Xiaoguo Zhou, Xianyu Yao & Yuanfa Li To cite this article: Sufang Yu, Guanghui She, Shaoming Ye, Xiaoguo Zhou, Xianyu Yao & Yuanfa Li (2018): Characteristics of soil microbial biomass and community composition in Pinus yunnanensis var. Tenuifolia secondary forests, Journal of Sustainable Forestry To link to this article: https://doi.org/10.1080/10549811.2018.1483250
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JOURNAL OF SUSTAINABLE FORESTRY https://doi.org/10.1080/10549811.2018.1483250
Characteristics of soil microbial biomass and community composition in Pinus yunnanensis var. Tenuifolia secondary forests Sufang Yua,b, Guanghui Shea, Shaoming Yeb,c, Xiaoguo Zhoub,c, Xianyu Yaob, and Yuanfa Lib,c a College of Forestry, Nanjing Forestry University, Nanjing, Jiangsu, China; bCollege of Forestry, Guangxi University, Nanning, Guangxi, China; cGuangxi Key Laboratory of Forest Ecology and Conservation, College of Forestry, Guangxi University, Nanning, Guangxi, China
ABSTRACT
Pinus yunnanensis var. Tenuifolia is an important species of timber and grease in southern China, but the characteristics of the soil microbial community in P. yunnanensis var. natural secondary forests are still poorly understood. Using a fumigation-extraction method and phospholipid fatty acid (PLFA) analysis, we study microbial biomass and community composition in the topsoil (0–10 cm) of three types of secondary forests (PYI, PYII, PYIII) dominated by P. yunnanensis var. to varing degrees. Microbial biomass carbon and nitrogen, total PLFA, and PLFA contents of bacterial, fungal, and arbuscular mycorrhizal fungi were significantly lower in PYI than PYII or PYIII, and there were significant differences in the monounsaturated/saturated fatty acid ratio among the tested forests. Principal component analysis indicated that the soil microbial community structure of the tested forests differed significantly. The changes in soil microbial biomass and community composition were positively correlated with soil water content, pH, organic matter (SOM), total nitrogen (TN), and total phosphorus. Season did not significantly affect the soil microbial community structure, but significantly affected soil microbial biomass, SOM, and TN, which were higher in the dry season than in the wet season.
KEYWORDS
P. yunnanensis var.; secondary forest; microbial community; phospholipid fatty acids; season
Introduction Soil microbes can drive biogeochemical cycles to control ecosystem functions, especially those of forests. Forest ecosystems do not rely on the input of artificial nutrition, but rather the nutrients in litter, root exudates, animal debris, and soil organic matter (SOM) are converted into minerals by soil microbes to maintain tree growth (Moore-Kucera & Dick, 2008; Rogers & Tate, 2001). Meanwhile, microorganisms promote the formation of soil aggregates through the entanglement of the bacteria and mycelia, resulting in the cementation of metabolites onto soil particles, thereby improving the structure of forest soil and increasing soil nutrients (Peng, Sheng, & Guo, 2010). Therefore, soil microorganisms have a critical role in restoring the fertility of disturbed and degraded forest soil.
CONTACT Yuanfa Li
[email protected] College of Forestry, Guangxi University, Nanning, Guangxi, China Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/wjsf. Supplemental data for this article can be accessed here. © 2018 Taylor & Francis
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Vegetation is an important factor that affects the soil microbial community in forest soil (Zi, Xiang, Wang, Ade, & Wang, 2017). Vegetation directly affects soil microbial community activity through litter input and root activity (Eviner & Chapin, 2003), and the impact intensity is related to plant species composition, age, and structure. Any changes to these forest characteristics subsequently alter the organic composition and yield of plant residues (Grayston, Vaughan, & Jones, 1997), which can in turn change the composition and function of the heterotrophic microbial community (Hobbie, Ogdahl, Chorover, Chadwick, & Oleksyn, 2007; Zak, Holmes, White, Peacock, & Tilman, 2003). Changes in vegetation also alter the forest abiotic environment (e.g., temperature, moisture, soil pH, soil nutrients), which can then affect the soil microbial community. Given that characteristics the abiotic environment change across seasons, characteristics of the soil microbial community such as soil microbial biomass, composition, and function, also tend to exhibit seasonal dynamics (Hong, Liu, Wang, & Yu, 2016; Kaiser et al., 2011; Moore-Kucera & Dick, 2008; Siles & Margesin, 2017; Wang, Shi, Luo, Liu, & Lu, 2013). Thus, exploring differences in soil microbial communities and seasonal variations in different forest communities should help clarify the mechanisms underlying the soil microbial community. The quantity and composition of soil microbial communities are sensitive to changes in soil quality. Soil microbial communities can reflect the changing processes of soil degradation earlier (Costa, Paixão, Caçador, & Carolino, 2007; Ma, Zhang, Xiao, Cui, & Yu, 2017), and are often used to characterize the structure and stability of soil ecosystems (Hong et al., 2016). In recent years, studies have reported on the effects of differences in stand type on soil microbial biomass and community structure, such as differences in stand age (Cao et al., 2010; Luo et al., 2017; Moore-Kucera & Dick, 2008; Wang, Han, & Wang, 2017), tree species (Grayston & Prescott, 2005; Hong et al., 2016; Thoms & Gleixner, 2013), gap size (Yang, Geng, Zhou, Zhao, & Wang, 2017), and degree of disturbance (Li, Zhang, Jiang, Xin, & Yang, 2006; Overby, Owen, Hart, Neary, & Johnson, 2015; Song et al., 2015). However, these studies mainly focused on plantations, and there is a lack of research on secondary forests. Analysis of soil microbial biomass and community structure in secondary forests may help to improve our understanding of the relationship between aboveground and underground communities. P. yunnanensis var. Tenuifolia is a geographical variety of conifer P. yunnanensis that migrated from the subtropical Yunnan Plateau to the east, adapting to the climate in the subtropical valley of South Asia (Li & Wang, 1981). Before the 1950s, the Nanpanjiang River Basin contained a large P. yunnanensis var. natural forest, which included a variety of community types. However, the majority of the primary P. yunnanensis var. forests disappeared following excessive logging and serious damage to their habitat between 1950 and 2000. Although growth of P. yunnanensis var. secondary forests occurred due to a protection policy implemented in the 21st century, the community areas and quality of the forests are lower (Li, Hui, Yu, Yao, & Ye, 2017). Presently, only a few studies have investigated P. yunnanensis var. forests. Some domestic scholars have studied the formation (Wang, 1987), genetic diversity of provenances (Yang, Feng, & Wu, 2014), geographic distribution (Li & Wang, 1981), dry shape (Lu & Li, 1997), wood properties (Qin, Liu, Lan, Fu, & Yang, 2015), and stand microstructure (Li et al., 2017) of P. yunnanensis var. forests. However, systematic research that may inform the restoration of P. yunnanensis var. communities is lacking.
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In this work, we studied three typical types of P. yunnanensis var. secondary forests with wide distribution in Yakang Orchidaceae National Nature Reserve of Guangxi, China. The physicochemical properties of the soil microbial biomass, as well as the phospholipid fatty acid (PLFA) characteristics of the microbial communities in the three types of stands were analyzed during the wet and dry seasons. The objectives of this study are as follows: 1) clarify the differences in soil microbial biomass and microbial community structures in different P. yunnanensis var. secondary forests; 2) reveal the relationship between abiotic environmental factors and soil microbial communities in different stand types; and 3) explore seasonal variation in soil microbial biomass and microbial community structures according to stand type.
Materials and methods Study area The study area was the Guangxi Yachang Orchid Plant National Nature Reserve, located in the transition zone between the temperate and subtropical zones, ranging from a longitude of 106°11′31″E to 106°27′0″E and a latitude of 24°44′16″N to 24°53′58″N, with an altitude ranging from 350–1,971 m. This nature reserve belongs to a typical karst landform area and has a typical subtropical monsoon climate with clear dry and wet seasons. The annual precipitation ranges from 941 mm to 1,217 mm, which mainly occurs during the summer season (May to August), and the annual evaporation ranges from 1,089 mm to 1,685 mm. As the evaporation rate is higher than the precipitation rate, the nature reserve belongs to the semi-dry climate zone. Since the area is strongly influenced by monsoon circulation and foehn effects, the summer season is wet with a humid air mass, while there is a cold air mass in the mainland in the winter. The average annual temperature is 16.0 ~ 20.4°C, and the average annual ground temperature is 20.1 ~ 24.2°C. The soil of this area is weakly acidic with high gravel content.
Experimental design Three types of P. yunnanensis var. secondary mixed forests were selected in the abovementioned nature reserve. The first stand is a mixed forest, in which P. yunnanensis var. is the dominant tree species (50%) and mixed broad-leaved species are also present (PYI). This stand self-recovered without anthropogenic disturbance after a fire in 1987. The second stand is a mixed forest containing P. yunnanensis var. (35%) and Quercus (40%) as the dominant tree species (PYII), which recovered after clear cutting in the 1960s. The third stand is a mixed forest containing P. yunnanensis var. (15%), Keteleeria davidiana (Bertr.) Beissn. (35%), and Quercus (40%) as the dominant tree species (PYIII), which selfrecovered after strong selection cutting in 1970s. Three 20 × 30 m sample plots were established along the diagonal in the upper, middle, and downhill areas of the PYI, PYII, and PYIII stands, respectively. The environmental (Table 1) and vegetation (Table S1) characteristics of these sample plots were studied in detail.
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Table 1. The main characteristics of the three secondary forests. Parameter Community type Location Mean altitude (m) Mean slope (°) Canopy cover Density (N/ha) Basal area (m2/ha) Soil type Gravel content (%) Thickness of liter (cm) (t·hm−2·a−1) Number ratio between conifer and hardwood species
PYI Pine-broadleaved mixed forest 106°19ʹ4.2” N 24°51ʹ15.9” E 1068.5 30.5 0.9 2931 33.399 Rendizina 40.6% 5.0–7.0 14.27 51.51%
PYII Pine-oak mixed forest 106°14ʹ14.6” N 24°47ʹ25.4” E 770.3 34 0.8 1592 22.55 Mountain yellow soil 35.4% 2.0–3.0 12.46 32.47%
PYIII Pine-cunninghamiaoak mixed forest 106°23ʹ10.6” N 24°49ʹ54.2” E 1253.9 12.2 0.85 1340 32.88 Rendizina 53.9% 3.0–5.0 12.59 39.30%
Soil sampling The climate in our study area is relatively dry in December and January, while humid in July and August, according to the monthly average temperatures and precipitation from 2014 to 2016. Accordingly, soil samples were collected in January (dry season) and August (wet season) of 2016. In each 20 × 30 m plot, five sampling points were randomly arranged at the diagonal after removing the litter. Five top soil samples in the 0–10 cm soil depth were obtained with an 8-cm-diameter steel tube, mixed, and placed in the sterile bags, which were stored with ice packs and transported to the laboratory immediately. The soil was passed through a sieve containing 2-mm holes after removing rocks and roots. Each sample was then divided into three parts. One part was freeze-dried and preserved in a –80°C freezer for determination of PLFA content, another was stored at 4°C for measurement of microbial biomass carbon (MBC) and microbial biomass nitrogen (MBN) within a week of collection, and the last part was naturally air dried to determine the physicochemical properties of the soil.
Laboratory analysis Soil water content (SWC), pH, SOM, total nitrogen (TN), total phosphorus (TP), total potassium (TK), nitrate nitrogen (NN), available phosphorus (AP), and available potassium (AK) were measured as described in Bao (2008). MBC and MBN were determined using the fumigation-extraction method (Lin, Wu, & Liu, 1999). PLFAs were extracted according to the method of Bossio and Scow (1998). PLFA 19:0 was used as an internal standard, and the samples were quantified using a gas chromatography - atomic emission detector (GC-AED) System (Hewlett-Packard 6890; Agilent Technologies, Santa Clara, CA, USA). Concentrations of PLFAs were calculated as nmol PLFA per g soil dry weight. The relative abundance of individual fatty acids was expressed as the proportion (mol %) of the sum of all fatty acids. We identified a total of 66 PLFAs in the soil samples. However, we only included fatty acids that were found in proportions higher than 0.3% in the analysis. Certain fatty acids (Table 2) are found in large amounts in specific groups of microorganisms and can be used for
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Table 2. Fatty acids used in the analysis of microbial communities structures. Organisms Bacteria
Diagnostic fatty acids 14:0, 16:0, 17:0, 18:0, 19:0, a15:0, i15:0, i16:0, a17:0, i17:0, 15:0 3OH, 16:1ω9c, 17:1ω8c, 18:1ω5c,cy17:0, cy19:0 a15:0, i15:0, i16:0, a17:0, i17:0
Reference Frostegård & Bååth (1996); Bossio & Scow (1998) G+ Frostegård & Bååth (1996); Bossio & Scow (1998) G– 15:0 3OH, 16:1ω9c, 17:1ω8c, 18:1ω5c, cy17:0, cy19:0 Zelles (1999); Zogg, Zak, Ringelberg, White, & Macdonald (1997) G–/G+ 15:0 3OH + 16:1ω9c + 17:1ω8c + cy17:0 + cy19:0/a15:0 + i15:0 Kourtev, Ehrenfeld, & Häggblom + i16:0 + a17:0 + i17:0 (2002) Fungi 18:1ω9c Olsson & Alström (2000) Fung/Bact 18:1ω9c/14:0 + 16:0 + 17:0 + 18:0 + 19:0 + a15:0 + i15:0 + i16:0 + a17:0 + i17:0 + 15:0 3OH + 16:1ω9c + 17:1ω8c + cy17:0 + cy19:0 AMF 16:1ω5c Olsson & Alström (2000) Monounsaturated 16:1ω9c, 17:1ω8c, 16:1ω5c, 18:1ω9c, 18:1ω5c Bossio & Scow (1998); Vestal & White (1989) Saturated 14:0, 16:0, 17:0, 18:0, 19:0, 20:0 Bossio & Scow (1998); Vestal & White (1989) Mono/Sat 16:1ω9c + 17:1ω8c + 16:1ω5c + 18:1ω9c + 18:1ω5c/ 14:0 + 16:0 + 17:0 + 18:0 + 19:0 + 20:0 Others 10Me17:0, 11Me18:1ω7c, 10Me18:0 TBSA, 20:4ω6,9,12,15c、16:0 N alcohol G–, Gram-negative bacteria; G+, Gram-positive bacteria; Fung, fungi; Bact, bacteria; AMF, arbuscular mycorrhizal fungi; Mono, monounsaturated; Sat, saturated.
diagnostic purposes. Diagnostic groups of fatty acids were used to calculate the following ratios: bacteria:fungi (Fung/Bact), Gram-negative:Gram-positive bacteria (G–/G+), and monounsaturated:saturated (Mono/Sat) fatty acids. Data processing Two-way analysis of variance (ANOVA) was performed to test the effects of stand type and season, and their interaction, on soil physicochemical properties, soil microbial biomass, and fatty acid content. Soil physicochemical variables and individual microbial variables among different stand types and seasons were also studied using one-way ANOVA. Multiple comparisons were performed using the least significant difference (LSD) method (p < 0.05). To examine the differences in soil microbial community structure among different stand types, 15 types of PLFA markers with significant differences (p < 0.05) were selected in the dry and wet seasons. Pearson correlation coefficients were used to identify the relationships between soil microbial biomass and environmental factors (soil physicochemical factors and topographic factors). Redundancy analysis (RDA) was used to study the relationship between individual fatty acids and environmental factors . All data analyses were performed using SPSS 19.0 for Windows (SPSS Inc., Munich, Germany), except for PCA and RDA, which were conducted using the Canoco 4.5 for Windows software (Microcomputer Power, Inc., Ithaca, NY). Figures were generated using SigmaPlot 12.0 (Systat Software Inc., San Jose, CA, USA). The site environment variables were converted and quantified before RDA. The aspect transformation was converted to a value between 0–1 using an azimuth of 0–360° (transformation of aspect, TRASP) (Roberts & Cooper, 1989), using the following
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conversion formula: TRASP = {1 – cos[(π/180)(aspect – 30)]}/2. Since the three plots of each secondary forests are located at the lower, middle and upper slopes, respectively, we defined the slope position of the plots as 1, 2 and 3 for the lower, middle, and upper slopes.
Results Soil physicochemical properties SWC, SOM, TN, TP, TK, AK, and AP were significantly affected by stand type and season, and the soil C/N ratio was significantly affected by season. The interaction between season and stand type had significant effects on SWC, SOM, TN, and TP. The soil pH of the three secondary forests during the dry and wet seasons was weakly acidic, ranging from 4.56–5.06. During the dry and wet seasons, the SWC, SOM, TN, TP, TK, AK, and AP were lowest in PYI. The SWC, SOM, and TN during the dry season were highest in PYIII, whereas they were highest in PYII during the wet season. SOM, TN, TP, TK, and AK were significantly higher during the dry season than the wet season (p < 0.05), whereas SWC, AP, and the C/N ratio were significantly lower during the dry season (p < 0.05) (Table 3). Soil microbial biomass and community structure The total soil microbe levels of PLFAs, bacteria PLFAs, fungi PLFAs, arbuscular mycorrhizal fungi (AMF) PLFAs, MBC, and MBN showed similar trends. During the dry season, they were highest in PYIII, followed by PYII and PYI. The differences between PYII and PYIII were not significant, but both were significantly higher than PYI (p < 0.05). During the wet season, the microbe levels of PYII were higher than those of PYIII, and the levels of both were significantly higher than in PYI (p < 0.05) (Figure 1). Moreover, the levels of total PLFAs, Bacteria PLFAs, Fungi PLFAs, AMF PLFAs, and MBC (p < 0.05) were significantly affected by seasonal variations, and were higher in the dry season than in the wet season (Figure 1, Table 5). All of the studied soil samples were dominated by bacterial fatty acids, and the relative abundance of 16:0 was highest among detected PLFAs in all three stand types (Table 4). One-way ANOVA for most of the individual PLFA variables tested showed that the abundance of the 15 types of PLFA markers differed significantly among the three studied soils in the dry and wet seasons, respectively (Table 4). Regarding the ratios of the diagnostic PLFAs (Figure 2), the G–/G+ ratios during the wet and dry seasons were higher in PYIII than in PYI, and significantly higher than in PYII (p < 0.05). The trends in the Mono/Sat ratios during the dry and wet seasons were similar, and the highest levels were found in PYIII, followed by PYII and PYI. The Fung/Bact ratios of the three secondary forests did not differ significantly between the dry and wet seasons. PCA based on 15 individual PLFA profiles clearly separated the three sampling sites during both the dry and wet seasons (Figure 3). During the dry season, the first two principle components (PCs) accounted for 89.7% of the variation in the dataset, with 64.6% explained by PC1. Microbial structure of PYI was located in the positive direction of PC1, while that of PYII and PYIII were concentrated in the negative direction of PC1 and occupied both PC1 sizes. PC1 was significantly correlated with 19:0 (Bacteria), 16:N
PYI 17.34 ± 0.95a 4.56 ± 0.04a 24.30 ± 2.43a 0.94 ± 0.12a 0.12 ± 0.01a 3.22 ± 0.80a 1.85 ± 0.56a 37.71 ± 11.21a 0.58 ± 0.13a 15.01 ± 0.33a
PYII 22.54 ± 0.86b 4.86 ± 0.02b 41.03 ± 0.70b 1.74 ± 0.09b 0.30 ± 0.01b 9.10 ± 0.15b 1.34 ± 0.85a 132.14 ± 9.16b 0.78 ± 0.13a 13.74 ± 0.84a
Dry season PYIII 25.36 ± 1.16c 5.06 ± 0.03c 48.23 ± 0.95c 1.86 ± 0.17b 0.25 ± 0.01c 2.83 ± 0.71a 2.17 ± 0.36a 40.06 ± 3.86a 0.72 ± 0.12a 15.09 ± 1.19a 19.05 4.67 21.39 0.75 0.10 1.45 0.61 29.86 1.62 18.17
PYI ± 1.12A ± 0.03A ± 2.48A ± 0.13A ± 0.00A ± 0.75A ± 0.73A ± 5.52A ± 0.23A ± 1.68A
PYII 28.56 ± 1.27B 4.84 ± 0.04B 36.16 ± 2.48B 1.36 ± 0.11B 0.29 ± 0.02B 8.41 ± 1.05B 1.32 ± 0.76A 109.68 ± 15.62B 2.53 ± 0.19B 16.22 ± 0.45A
Wet season PYIII 26.92 ± 1.59B 4.72 ± 0.03A 35.56 ± 1.84B 1.27 ± 0.02B 0.21 ± 0.03C 2.00 ± 0.49A 2.00 ± 0.87A 31.94 ± 6.82A 1.93 ± 0.14A 17.18 ± 1.12A
S *** *** *** *** ** ** * *** ***
*** ***
Two-way ANOVA T *** *** *** *** *** ***
T, Stand type; S, season; Data with different letters are significantly different (p < 0.05). Asterisks indicate significant ANOVAs. *p < 0.05, **p < 0.01, and ***p < 0.005.
Variables SWC/% pH SOM/g·kg−1 TN/g·kg−1 TP/g·kg−1 TK/mg·kg−1 NN/mg·kg−1 AK/mg·kg−1 AP/mg·kg−1 C/N
Table 3. Characteristics of 0–10 cm of soil of the three P. yunnanensis var. secondary forests (mean ± SE; n = 3).
**
T×S ** ** ** **
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Figure 1. Fatty acid classes in the three P. yunnanensis var. secondary forests. Data with different letters are significantly different (p < 0.05) (n = 3).
alcohol (Others), and 18:1ω9c (Fungi). PC2 was significantly correlated with i15:0 (G+), 16:0 (Bacteria), and i17:0 (G+). During the wet season, the first two PCs accounted for 92.0% of the variation in the dataset, and the contribution of PC1 was 70.1%. Similar to the dry season, microbial structure of PYI was located in the positive direction of PC1, while that of PYII and PYIII were concentrated in the negative direction and occupied both PC1 sizes. PC1 was significantly correlated with 19:0 (Bacteria), 16:N alcohol (Others), 18:1ω5c (G–), and 11Me18:1ω7c (Others). PC2 was significantly correlated with 16:1ω9c (G–), 18:1ω9c (Fungi), and i15:0 (G+).
Relationship among measured environmental variables, soil microbial biomass and structure Correlation analysis showed that the contents of MBC, MBN, and SWC, as well as pH, SOM, TN, and TP were significantly positively correlated during the dry and wet seasons (Table 6). During the dry season, there was a significant positive correlation between soil microbial PLFAs and SWC, pH, SOM, and TN. During the wet season, there was a
PLFAs a15:0 i15:0 i16:0 a17:0 i17:0 15:0 3OH 16:1ω9c cy17:0 17:1ω8c cy19:0 18:1ω5c 14:0 16:0 17:0 18:0 19:0 18:1ω9c 16:1ω5c 10Me17:0 10Me18:0 TBSA 11Me18:1ω7c 20:4ω6,9,12,15c 16:0 N alcohol
PYI 2.61 ± 0.24 8.69 ± 0.71 4.03 ± 0.32 1.62 ± 0.09 1.95 ± 0.11 1.38 ± 0.12 0.80 ± 0.14 1.78 ± 0.13 0.51 ± 0.02 11.64 ± 0.35 1.36 ± 0.12 0.71 ± 0.05 12.40 ± 0.18 0.70 ± 0.01 2.92 ± 0.08 2.34 ± 0.11 6.57 ± 0.20 2.42 ± 0.16 0.80 ± 0.07 2.77 ± 0.18 0.72 ± 0.14 0.67 ± 0.07 2.51 ± 0.13
PYIII 3.61 ± 0.72 7.48 ± 0.60 3.41 ± 0.33 1.90 ± 0.37 1.84 ± 0.13 1.60 ± 0.33 1.15 ± 0.13 1.72 ± 0.16 0.54 ± 0.08 11.14 ± 1.50 1.83 ± 0.35 0.76 ± 0.09 11.92 ± 0.63 0.54 ± 0.03 2.72 ± 0.10 1.03 ± 0.25 9.30 ± 1.69 3.15 ± 0.35 0.79 ± 0.11 3.40 ± 0.31 1.18 ± 0.12 0.76 ± 0.14 1.00 ± 0.17
Dry season PYII 3.10 ± 0.11 9.50 ± 0.31 5.02 ± 0.19 1.91 ± 0.15 2.55 ± 0.30 2.19 ± 0.30 0.83 ± 0.04 1.37 ± 0.10 0.52 ± 0.05 10.39 ± 1.12 1.66 ± 0.04 0.79 ± 0.04 12.66 ± 0.52 0.64 ± 0.02 2.95 ± 0.06 1.35 ± 0.29 8.20 ± 0.62 2.83 ± 0.14 0.99 ± 0.06 3.38 ± 0.30 0.97 ± 0.05 0.80 ± 0.11 1.27 ± 0.28
Asterisks indicate significant ANOVAs. *p < 0.05, **p < 0.01, and ***p < 0.005.
NO. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 ***
*** * ** * * * * **
** * * *
* **
Significance
PYI 2.84 ± 0.28 8.30 ± 0.21 4.35 ± 0.28 1.67 ± 0.13 2.41 ± 0.12 1.55 ± 0.03 0.64 ± 0.02 1.63 ± 0.14 0.46 ± 0.03 9.91 ± 0.37 1.31 ± 0.07 0.84 ± 0.04 13.19 ± 0.28 0.65 ± 0.02 3.08 ± 0.12 2.69 ± 0.17 7.68 ± 0.11 1.78 ± 0.11 0.89 ± 0.04 3.19 ± 0.25 0.62 ± 0.08 0.43 ± 0.03 6.39 ± 0.51
PYII 3.07 ± 0.19 8.69 ± 0.73 5.29 ± 0.28 1.98 ± 0.17 2.92 ± 0.09 1.87 ± 0.22 0.59 ± 0.04 1.36 ± 0.13 0.46 ± 0.03 11.26 ± 0.55 1.49 ± 0.11 0.76 ± 0.03 13.38 ± 0.12 0.73 ± 0.01 3.53 ± 0.06 1.41 ± 0.28 7.23 ± 0.27 2.30 ± 0.33 1.05 ± 0.06 3.67 ± 0.50 1.19 ± 0.16 0.59 ± 0.10 3.12 ± 0.59
PYIII 3.17 ± 0.63 7.14 ± 0.31 3.72 ± 0.24 1.81 ± 0.20 2.23 ± 0.18 1.68 ± 0.50 0.90 ± 0.04 1.65 ± 0.22 0.50 ± 0.07 12.26 ± 0.42 1.46 ± 0.18 0.74 ± 0.06 13.09 ± 0.56 0.66 ± 0.04 3.19 ± 0.27 1.69 ± 0.16 8.74 ± 0.96 2.24 ± 0.11 0.83 ± 0.03 4.12 ± 0.90 1.07 ± 0.24 0.37 ± 0.04 3.68 ± 0.31
Wet season
Table 4. Presence and proportion of major PLFAs (%) in the three P. yunnanensis var. secondary forests (means±SE, n = 3).
* * ***
* * ** * * **
*
**
***
**
* **
Significance
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Table 5. Effects of stand type, season, and their interaction on microbial biomass (n = 3). T S T×S
MBC
