Soil Microbial Biomass Analysis Essay

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  • Abstract

    Aims

    Natural secondary forest (NSF) and larch plantation are two of the predominant forest types in Northeast China. However, how the two types of forests compare in sustaining soil quality is not well understood. This study was conducted to determine how natural secondary forest and larch plantation would differ in soil microbial biomass and soil organic matter quality.

    Methods

    Microbial biomass carbon (MBC), microbial biomass nitrogen (MBN), soil organic carbon (SOC) and total nitrogen (TN) in the 0- to 15-cm and 15- to 30-cm soil layers were investigated by making chemical and biological measurements in the montane region of eastern Liaoning Province, Northeast China, during the growing season of 2008 in stands of NSF and Larix olgensis plantation (LOP).

    Important Findings

    We found that soil MBC and MBN were significantly lower in the LOP than in the NSF. Both MBC and MBN declined significantly with increasing soil depth in the two types of stands. The ratios of MBC to SOC (MBC/SOC) and MBN to TN (MBN/TN) were also significantly lower in the LOP than in the NSF. Moreover, the values of MBC, MBC/SOC, and MBN/TN significantly varied with time and followed a similar pattern during the growing season, all with an apparent peak in summer. Our results indicate that NSF is better in sustaining soil microbial biomass and nutrients than larch plantation in the temperate Northeast China. This calls for cautions in large-scale conversions of the native forests to coniferous plantations as a forest management practice on concerns of sustaining soil productivity.

    microbial biomass carbon, microbial biomass nitrogen, soil quality, temperate forests

    INTRODUCTION

    Natural secondary forests (NSFs) widely occur in temperate regions around the world (Wang and Yang 2007). They are formed through natural regeneration following stand-replacing disturbances of primary forests by anthropogenic activities or by extreme natural events (Zhu and Liu 2007). In Northeast China, NSFs account for as much as 70% of the regional forests (Hao et al. 2000), of which large areas have been turned into larch plantations for fast timber production since the 1950s (Wang et al. 2006). However, concerns for biological conservation of forest resources and mitigation of climate change in the context of global change have created large interests in assessing the changes of soil quality and carbon sequestration capacity with conversion of natural forests to plantations and/or other land uses (Chen and Li 2003; Shi et al. 2009; Wang and Yang 2007; Yang et al. 2007).

    There is a widespread perception that plantations are generally inferior to naturally regenerated forest stands in nutrient cycling and soil quality (Burton et al. 2007; Liu et al. 1998); while this view is supported by a large body of literature (Behera and Sahani 2003; Kasel and Bennett 2007; Yan et al. 2008; Zhang et al. 2009), there also exist contradictory experimental results. For example, the study of Li et al. (2005) shows that there is no significant difference in total soil organic carbon (SOC) between a pine plantation (5.59 ± 0.09 kg m−2) and a secondary mixed forest (5.68 ± 0.16 kg m−2) in Puerto Rico. Our previous study also indicates that NSF and Larix olgensis plantation (LOP) differ in chemical composition of soil P but not in overall P availability in eastern Liaoning Province of Northeast China (Yang et al. 2010), where L. olgensis is an introduced species and known to have a slow rate of litter decomposition than the tree species in the natural forests (Liu et al. 1998). A conversion from natural forest to coniferous plantation is generally found to reduce SOC stock (Guo and Gifford 2002) and result in a decline in soil organic matter quality (Behera and Sahani 2003; He et al. 2006).

    Soil microbial biomass, which can be either a source or sink of available nutrients, plays a critical role in nutrient transformation in terrestrial ecosystems (Singh et al. 1989). Any change in the microbial biomass may affect soil organic matter turnover. Thus, the soil microbial activity has a direct influence on ecosystem stability and fertility (Smith et al. 1993). In general, microbial biomass can be used for assessing soil quality of different types of vegetation (Groffman et al. 2001; Zeng et al. 2009) as well as for evaluating soil perturbation and restoration (Ross et al. 1982; Smith and Paul 1990). Forest types influence soil microbial biomass and activities by determining the quantity and quality of organic matter inputs (Hackl et al. 2004; Xu et al. 2008). Shifts in plant community composition may affect SOC dynamics as a result of changes in the amount and chemical composition of plant residues returned to the soil (Jin et al. 2010), which in turn influences the pool size and activity of the soil microbial biomass (Kasel and Bennett 2007).

    Besides forest types, seasonal variations of temperature, rainfall, plant development, and organic matter accumulation from litterfall also have great influences on soil microbial biomass (Chen et al. 2005; Devi and Yadava 2006; Maithani et al. 1996; Tonon et al. 2005). Fluctuations in the size of soil microbial biomass during the growing season are considered an important factor in controlling the turnover of soil carbon and nitrogen. Therefore, information on seasonal variations in soil microbial biomass is needed to improve our understanding on soil nutrient transformation and availability. In temperate forests, however, several studies on the seasonal dynamics of soil microbial biomass in recent decades have reported inconsistent findings (e.g. Bauhus and Barthel 1995; Gallardo and Schlesinger 1994; Zhu and Carreiro 2004). For instance, there are studies demonstrating that the maximum values of microbial biomass may occur in summer (Gallardo and Schlesinger 1994; Zhong and Makeschin 2006) or spring (Diaz-Raviña et al. 1995; Zhu and Carreiro 2004) or show no significant seasonal variations (Bauhus and Barthel 1995). The variable results suggest that seasonal variations in soil microbial biomass probably depend on the specific forest ecosystems and climatic factors.

    In this study, the concentrations of microbial biomass C and N, soil organic C, total N and the relationships between soil organic matter and microbial biomass in the 0- to 15-cm and 15- to 30-cm soil layers were compared between the stands of NSF and LOP in the montane region of eastern Liaoning Province, Northeast China. The specific objectives of this study were to determine: (1) how NSFs and larch plantations would differ in soil microbial biomass and soil organic matter quality and (2) how soil microbial biomass would vary seasonally for the two forest types of forests under temperate climatic conditions. We hypothesize that the stands of LOP would have markedly lower soil microbial biomass and activity, hence slower nutrient cycling than the NSFs because of the slower rate of litter decomposition in L. olgensis than in the tree species in the NSFs.

    MATERIALS AND METHODS

    Site description and experimental design

    The study was conducted at Qingyuan Experimental Station of Forest Ecology of Institute of Applied Ecology, Chinese Academy of Sciences. The station is located in a mountainous region in the eastern Liaoning Province, Northeast China (latitude 41°51'N, longitude 124°54'E, elevation 500–1100 m above sea level). The climate of the region is a continental monsoon type with a humid and rainy summer and a cold and snowy winter. Mean annual air temperature varies between 3.9 and 5.4°C with the minimum of −37.6°C in January and the maximum of 36.5°C in July. The mean annual precipitation ranges between 700 and 850 mm, of which 80% rain falls in June, July and August. The frost-free period lasts for 130 days on average, with an early frost in October and late frost in April (Zhu et al. 2007). The mean monthly temperature and precipitation during the growing season of 2008 are shown in Fig. 1. The soil type is a typical brown forest soil with a thickness of 60–80 cm. The brown forest soil belongs to Udalfs according to the second edition of US Soil Taxonomy (1999).

    Figure 1:

    mean monthly air temperature and precipitation in the study area in 2008 in eastern Liaoning Province, Northeast China.

    Figure 1:

    mean monthly air temperature and precipitation in the study area in 2008 in eastern Liaoning Province, Northeast China.

    The study site was originally occupied by primary mixed broadleaved-Korean pine (Pinus koraiensis Sieb. et Zucc.) forests until 1930s and subsequently subjected to decades of unregulated timber removal. A large fire in the early 1950s cleared off the entire forests and the site was gradually replaced by a mixture of naturally regenerating broadleaved native tree species. Since 1960s, patches of the secondary natural forests were cleared and replaced by larch (L. olgensis or Larix keampferi [Lamb.] Carr.) plantations (Wang and Yang 2007).

    Our sample plots were set up on three stands of NSF and three stands of LOP of the age 16–44 years. The six stands have a similar topographical feature and the underlying soils are developed from the same parental materials. In each of the stands, three 20 × 20-m plots were laid out in September 2006. The NSF plots consisted of the tree layer, the understory component and the herbage component. The tree layer includes Juglans mandshurica Maxim., Quercus mongolica Fischer ex Ledebour, Acer mono Maxim., Fraxinus rhynchophylla Hance and Ulmus macrocarpa Hance; the understory component includes Acer triflorun Kom., Acer tegmentosum Maxim, Asarum hetelotopoides var. mandshricum Maxim. and Syringa amurensis Rupr; and the herbage component includes Cardamine leucantha (Tausch) OE Schulz, Allium monanthum Maxim., Arisaema amurense Maxim., and Polygonatum involucratum Maxim. The LOP plots contain the shrub layer and the herbage layer. The shrub layer includes A. tegmentosum, Acer pseudo-sieboldianum (Pax.) Kom., Schisandra chinensis (Turcz.) Bail., Syringa wolfi Schneid. and Acanthopanax senticosus (Rupr. et Maxim.) Harms., and the herbage layer includes C. leucantha, Rubia sylvatica Nakai and Spuriopimpinella brachycarpa (Kom.) Kitag.

    Soil sampling and chemical analysis

    In spring, summer and autumn, mineral soil samples at 0- to 15-cm and 15- to 30-cm depths were collected on 25 April, 18 July and 19 September 2008, respectively. The experimental design did not include winter season because of the snow cover and frozen soils, which made the winter sampling impractical. Litter layer was removed before mineral soil sampling. Nine soil samples were randomly collected on each plot using a stainless cylinder with 5 cm diameter. The samples collected from each stand were mixed and homogenized by compositing soils at the same depth from three plots. The composited soils were divided into two parts: one was sieved to pass through a 2-mm mesh immediately and stored at 4°C until analysis for the estimation of microbial biomass carbon (MBC) and microbial biomass nitrogen (MBN), the other air-dried and passed through a 0.25-mm sieve for SOC and total nitrogen (TN).

    The SOC and TN were analyzed by dry combustion on a Vario EL III elemental analyzer (Elementar Analysensysteme GmbH, Hanau, Germany). Soil MBC and MBN were determined by fumigation–extraction method (Brookes et al. 1985; Vance et al. 1987a). For each plot, three out of six subsamples (each 10.0 g fresh soil) were fumigated with ethanol-free chloroform for 24 h at 25°C in an evacuated extractor. The remaining samples were treated as control. Fumigated and non-fumigated soils were extracted with 40 ml 0.5 mol l−1 K2SO4 (soil:extractant = 1:4) and shaken for 1 h on a reciprocal shaker. The extracts were filtered using Whatman No.42 filter paper with diameter 7 cm and frozen stored at −15°C prior to analysis. The total organic carbon and nitrogen in the extracts were measured using a Multi N/C 3000 analyzer (Elementar Analysensysteme GmbH). Soil pH was estimated on a 1:2.5 soil–water mixture. Gravimetric soil water content was calculated from mass loss after drying for 12 h at 105°C separately for the 0- to 15 and 15- to 30-cm soil layers. The forest floor litter was determined using six mesh traps (each an area of 1.0 m2) installed 0.5 m above the soil surface. The fine root (<2mm in diameter) biomass was estimated from eight soil cores (each an area of 28.3 cm2) in each plot. (Table 2) All data are expressed on oven-dry (105°C) soil weight basis.

    MBC was calculated as:where EC = (organic C extracted from fumigated soils) − (organic C extracted from non-fumigated soils) and kEC = 0.45 (Wu et al. 1990).
    MBN was calculated as:where EN = (total N extracted from fumigated soils) − (total N extracted from non-fumigated soils) and kEN = 0.54 (Brookes et al. 1985).

    Statistical analysis

    The statistical analyses were conducted with SPSS 11.5. A three-way analysis of variance (ANOVA) was used to test the effects of forest type, sampling season, and soil depth on soil microbial biomass and soil chemical properties. Pearson's correlation analysis was used to determine whether there were significant interrelationships among the measured properties of the soils.

    RESULTS

    The SOC and TN concentrations

    Concentration of SOC and C/N ratio differed significantly between NSF stands and LOP stands (P < 0.05). Moreover, the concentration of SOC in the 0- to 15-cm layer of the NSF stands was significantly higher than that in the LOP stands (Table 1). However, there was no significant difference in TN concentration between the NSF and LOP stands (Table 1). Concentrations of SOC and TN decreased with depth and did not differ among the seasons (Table 1). The C/N ratios of two soil layers (0–15 cm and 15- to 30-cm depth for the two forests) ranged from 10.0 to 13.1 and were significantly (P < 0.01) affected by sampling seasons.

    Table 1:

    three-way ANOVA on SOC, TN, C/N and soil water contents in the 0- to 15 and 15- to 30-cm soil layers in the NSF and the LOP in eastern Liaoning Province, Northeast China

    Soil depth (cm) Season Forest type SOC (g kg−1TN (g kg−1C/N Soil water content (%) pH 
    0–15 Spring NSF 50.5 ± 4.2 4.2 ± 0.4 12.3 ± 0.3 30.4 ± 1.2 5.82 ± 0.03 
    LOP 34.7 ± 1.8 3.2 ± 0.2 10.9 ± 0.2 27.5 ± 0.6 5.55 ± 0.05 
    Summer NSF 49.8 ± 3.9 3.9 ± 0.4 13.1 ± 0.3 34.8 ± 1.1 nd 
    LOP 37.0 ± 2.4 3.3 ± 0.3 11.4 ± 0.2 30.9 ± 0.8 nd 
    Autumn NSF 46.7 ± 4.6 3.7 ± 0.5 12.9 ± 0.2 26.7 ± 0.9 nd 
    LOP 36.8 ± 2.0 3.2 ± 0.2 11.4 ± 0.1 21.7 ± 1.3 nd 
    15-30 Spring NSF 23.4 ± 2.8 2.2 ± 0.3 11.1 ± 0.2 24.0 ± 0.5 5.91 ± 0.05 
    LOP 24.0 ± 1.9 2.4 ± 0.1 10.0 ± 0.2 22.9 ± 1.2 5.71 ± 0.04 
    Summer NSF 22.5 ± 2.0 2.0 ± 0.2 11.6 ± 0.2 25.2 ± 1.9 nd 
    LOP 23.4 ± 1.9 2.2 ± 0.1 10.6 ± 0.1 25.0 ± 0.7 nd 
    Autumn NSF 25.8 ± 3.2 2.2 ± 0.3 11.7 ± 0.2 19.6 ± 0.8 nd 
    LOP 25.5 ± 0.9 2.4 ± 0.1 10.4 ± 0.1 18.5 ± 1.2 nd 
    Three-way ANOVA results 
    Forest type ns ** ** 
    Sampling season ns ns ** ** 
    Depth ** ** ** ** 
    Forest × season ns ns ns ns 
    Forest × depth ns ns ** 
    Season × depth ns ns ns ns 
    Forest × season × depth ns ns ns ns 
    Soil depth (cm) Season Forest type SOC (g kg−1TN (g kg−1C/N Soil water content (%) pH 
    0–15 Spring NSF 50.5 ± 4.2 4.2 ± 0.4 12.3 ± 0.3 30.4 ± 1.2 5.82 ± 0.03 
    LOP 34.7 ± 1.8 3.2 ± 0.2 10.9 ± 0.2 27.5 ± 0.6 5.55 ± 0.05 
    Summer NSF 49.8 ± 3.9 3.9 ± 0.4 13.1 ± 0.3 34.8 ± 1.1 nd 
    LOP 37.0 ± 2.4 3.3 ± 0.3 11.4 ± 0.2 30.9 ± 0.8 nd 
    Autumn NSF 46.7 ± 4.6 3.7 ± 0.5 12.9 ± 0.2 26.7 ± 0.9 nd 
    LOP 36.8 ± 2.0 3.2 ± 0.2 11.4 ± 0.1 21.7 ± 1.3 nd 
    15-30 Spring NSF 23.4 ± 2.8 2.2 ± 0.3 11.1 ± 0.2 24.0 ± 0.5 5.91 ± 0.05 
    LOP 24.0 ± 1.9 2.4 ± 0.1 10.0 ± 0.2 22.9 ± 1.2 5.71 ± 0.04 
    Summer NSF 22.5 ± 2.0 2.0 ± 0.2 11.6 ± 0.2 25.2 ± 1.9 nd 
    LOP 23.4 ± 1.9 2.2 ± 0.1 10.6 ± 0.1 25.0 ± 0.7 nd 
    Autumn NSF 25.8 ± 3.2 2.2 ± 0.3 11.7 ± 0.2 19.6 ± 0.8 nd 
    LOP 25.5 ± 0.9 2.4 ± 0.1 10.4 ± 0.1 18.5 ± 1.2 nd 
    Three-way ANOVA results 
    Forest type ns ** ** 
    Sampling season ns ns ** ** 
    Depth ** ** ** ** 
    Forest × season ns ns ns ns 
    Forest × depth ns ns ** 
    Season × depth ns ns ns ns 
    Forest × season × depth ns ns ns ns 

    View Large

    Table 2:

    forest litterfall (t hm−2 y1) and fine root biomass (g m−2) to 30-cm soil depth for the NSF and the LOP in eastern Liaoning Province, Northeast China

    Forest type Forest litterfall Fine root biomass 
    NSF 2.99 295.3 
    LOP 1.15 114.4 
    Forest type Forest litterfall Fine root biomass 
    NSF 2.99 295.3 
    LOP 1.15 114.4 

    View Large

    Soil MBC, MBN, MBC/SOC and MBN/TN ratios

    Results from three-way ANOVA indicated that both forest types and sampling seasons had significant effects on MBC concentration; the soil MBN was significantly affected by forest types, but not the sampling season. The MBC and MBN concentrations were also significantly influenced by soil layers (P < 0.01). In general, concentrations of MBC and MBN showed higher values in the NSF stands than in the LOP stands across seasons (Fig. 2, P < 0.01). The values of soil MBC and MBN were 30–38% and 18–34% lower in the 0- to 15-cm layer and 27–37% and 7–28% lower in the 15- to 30-cm layer, respectively, in the LOP stands than in the NSF stands across the three seasons (Fig. 2). There was significant seasonal variation in soil MBC for both forest types (P < 0.05); soil MBC concentration was higher in summer than in spring and autumn at both soil depths. Soil MBN did not display apparent seasonal patterns (Fig. 2).

    Figure 2:

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