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Forests 2019, 10(9), 788; https://doi.org/10.3390/f10090788

Article
Effects on Greenhouse Gas (CH4, CO2, N2O) Emissions of Conversion from Over-Mature Forest to Secondary Forest and Korean Pine Plantation in Northeast China
by 1,2 and 1,2,*
1
Center for Ecological Research, Northeast Forestry University, No.26 Hexing Road Xiangfang District, Harbin 150040, China
2
Ministry of Education Key Laboratoty of Sustainable Forest Ecosystem Management, Northeast Forestry University, No.26 Hexing Road Xiangfang District, Harbin 150040, China
*
Author to whom correspondence should be addressed.
Received: 14 August 2019 / Accepted: 9 September 2019 / Published: 11 September 2019

Abstract

:
This study aimed to evaluate the seasonal variations of Greenhouse Gas fluxes (CH4, CO2, and N2O), Greenhouse Gas (GHG) emissions, and Global Warming Potential (GWP) over the extent of the regions and understand the controlling factors. CH4, CO2, and N2O fluxes were measured along with their environmental variables from the over-mature forest, Korean pine plantation, and five 60-year-old natural secondary forests in mountainous regions in Northeast China from May 2015 to April 2016. The results revealed that secondary forests, except for Betula platyphylla forest, significantly increased CH4 absorption by 19.6% to 51.0% and 32.6% to 67.0% compared with over-mature forest (OMF) and Korean pine plantation (KPP). Five secondary forests significantly increased CO2 flux by 32.9% to 78.6% and 14.1% to 53.4% compared with OMF and KPP, respectively. According to the annual statistics, the N2O fluxes had significant differences among seven forest types and decreased in the following order: mixed deciduous forest (MDF) > OMF > KPP > Populous davidiana forest (PDF) > hardwood forest (HWF) > Mongolian oak forest (MOF) > Betula platyphylla forest (BPF). The CH4 absorption and CO2 emission peaks occurred in summer, while the peak N2O fluxes occurred in spring. Stepwise multiple linear regression showed that CH4 and CO2 fluxes from soils were strongly influenced by air and soil temperature, soil volumetric water content (SVWC), nitrate nitrogen (NO3-N), ammonium nitrogen (NH4+-N), and soil organic carbon (SOC) across the whole year. Air temperature, SVWC, pH, NO3-N, and NH4+-N were the dominant factors controlling N2O fluxes from OMF and five secondary forests (except for BPF). No significant relationships were observed between these environmental factors and N2O fluxes from KPP and BPF. Additionally, the total cumulative CH4, CO2, and N2O fluxes were –13.37 t CH4 year−1, 41,608.96 t CO2 year−1, and 3.24 t N2O year−1, and the total cumulative GWP were 42,151.87 t CO2 eq year−1 through the whole year in seven forest types at the Maoershan Ecosystem Research Station in Northeast China. For the annual GWP per hectare, secondary forests and KPP averaged a higher GWP by 33.7%–80.1% and 17.9% compared with OMF. This indicates that the effects of early human activities have not been completely eliminated in the middle stage of KPP and secondary forests.
Keywords:
secondary forests; over-mature forests; Korean pine plantation; greenhouse gas fluxes; global warming potential

1. Introduction

The emissions of Greenhouse Gases (GHGs), such as CH4, CO2, and N2O, are a natural phenomenon that has been recognized to contribute to more than 90% of the anthropogenic climate warming [1]. According to the 2013 Intergovernmental Panel on Climate Change (IPCC) assessment, atmospheric concentrations of these gases have exceeded the pre-industrial levels by 40%, 150%, and 20%, respectively [2]. Additionally, they have already raised the global average surface temperature by 0.39 °C (0.69–1.08 °C) between 1901 and 2012 [3]. Although many studies consider CO2 to be the most important greenhouse gas, CH4 and N2O also play major roles in terrestrial ecosystems [4,5]. Forest soils have been identified to be a significant sink for atmospheric CH4, and it is estimated that CH4 uptake activities of soils represent 3%–9% of the global atmospheric CH4 sinks [6]. It has also been identified as a significant source for N trace gases, accounting for 60% of the total annual N2O emissions [7,8]. Additionally, with a span of 100 years, the global warming potential of CH4 and N2O is 28 and 265 times that of CO2, respectively [9]. However, few researchers simultaneously observed all three greenhouse gases over a full year in the temperate region of Asia, and thus a comprehensive study into the exchanges of CH4, CO2, and N2O between terrestrial ecosystems and the atmosphere is much needed.
The forest ecosystem plays a crucial role in regulating the atmospheric GHG concentration, especially after conversion from over-mature forest to secondary forest and plantations; the forest ecosystem can greatly affect soil GHG fluxes due to the variability of vegetation by altering the critical biogeochemical cycling of C and N and microbial activity [10]. However, the impact of forest conversion on GHG emissions remains uncertain: some studies have shown that it increases the seasonal emission of CO2 and uptake of CH4 [11], decreases N2O emission [12], or increases CH4 emission [13]; a study by Maljaned et al. [14] also revealed no effect on GHG emissions or uptakes. These differences are mainly due to the variations in tree species and vegetation composition, as well as soil chemical and physical properties such as texture, temperature, water content, and nutrient content [15]. The variability of vegetation in forests leads to the changes in the biogeochemical cycling of C and N and microbial activities. Therefore, natural secondary forest and plantations can greatly affect soil GHG fluxes. More attention should be paid to the influence of secondary forest and plantations on GHG emissions, which could help to improve the understanding of their effects on climatic variation [16].
The CH4, CO2, and N2O fluxes are strongly controlled by multiple factors, including air and soil temperature, soil volume water content, vegetation, and substrate availability [4,17,18]. Specifically, CH4 fluxes are the result of the balance between production and consumption [19]. Soil organic matter decomposition and respiration by litter and roots contribute to CO2 emissions [20]. N2O is mainly produced in soils because of two microbial activities: microbial nitrification (aerobic conditions) and denitrification (anaerobic conditions) [21]. The CH4, CO2, and N2O fluxes have rather large variations due to the complex processes of GHG fluxes and the variability of their controlling factors [4]. Therefore, exploring the main factors which play a decisive role in influencing GHG emissions is becoming increasingly critical.
The temperate mixed forest in East Asia ranks with that in northeastern North America and Europe as three largest portions of temperate mixed forests in the world. The temperate forest in Northeast China, as the predominant distribution area for the Asian temperate mixed forest, accounts for 35.3% and 34.5% of forested area and standing tree volume in the whole nation, respectively [22,23]. Since the turn of the 20th century, the Chinese government, Russians, and Japanese have destroyed the pristine forests by large-scale industrial logging. Subsequently, the pristine forests were replaced by secondary forests and plantations [24]. Smith et al. [25] indicated that land use practices have a significant influence on the environmental factors controlling GHG fluxes, and thus GHG fluxes. Therefore, the contribution of GHG fluxes to the global warming potential (GWP) might be changed in different land use types; to reveal the influence of forest types on GWP and regulate it, it is necessary to quantify how much each forest type contributes to GWP. However, studies on the contribution of different forest types to GWP are still limited. Thus, the representative forest conversion sequence with a clear land-use history, located at the Maoershan Ecosystem Research Station of Northeast Forestry University in Northeast China, provides an excellent opportunity to conduct valuable research on the evaluation of GHG emissions and GWP among the over-mature forest, secondary forest, and Korean pine plantation in the study region.
The objectives of the present study are (1) to identify the main factors controlling the GHG variations; (2) to evaluate GHG emissions and GWP in different land uses at the Maoershan Ecosystem Research Station of Northeast Forestry University in Northeast China. In order to achieve these objectives, CH4, CO2, and N2O fluxes were measured monthly from May 2015 to April 2016. Additionally, the air and soil temperature, soil volumetric water content (cm3 H2O cm–3 soil), soil pH, soil N content (NO3 and NH4+), and soil organic carbon were also measured. The results are useful for estimating the influence of conversion from over-mature forest to secondary forest and Korean pine plantation on GHG changes and GWP.

2. Materials and Methods

2.1. Study Area

Field experiments were conducted from May 2015 to April 2016 at the Maoershan Ecosystem Research Station of Northeast Forestry University in Northeast China (45°20′–25′N, 127°30′–34′E; 300 m.a.s.l.). The region represents typical forest types and landscape in Northeast China with a dark brown forest soil, and the parent material is granite bedrock _ENREF_1_ENREF_1 [26]. This region belongs to the temperate climate zone, which experiences four distinct seasons and is characterized by strong monsoon winds in spring, a warm and humid summer, a mild and cool autumn, and a dry and cold winter. The mean annual temperature is 4.9 °C and mean minimum and maximum air temperature are –21.5 and 32 °C, respectively. The total annual precipitation ranges from 600 to 800 mm, about 80% of which results from the rain during the summer months and the snow during the winter months, and the other 20% comes from hail and rime [27].
A typical land-use sequence in this region was selected, including over-mature mixed broadleaved-Korean pine forest, five secondary forests, and Korean pine plantation (KPP). See Figure 1 for seven forest types distribution. The over-mature forest (OMF) is dominated by Korean pine forest mixed with such deciduous species as Betula spp., Populus spp., Quercus spp., etc. The OMF, located at the mountain top, has not been disturbed by human activities. The secondary forests in the study area were classified into five types by dominant vegetation: Mongolian oak forest (MOF), mixed deciduous forest (MDF), Betula platyphylla forest (BPF), Populous davidiana forest (PDF), and hardwood forests (HWF) ranging in age from 60 to 66 years. They were regenerated naturally following the harvest of over-mature mixed broadleaved-Korean pine forest in 1949, 1950, 1950, 1950, and 1955, respectively. The 60-year-old pure Korean pine plantation was also converted from OMF by reforestation in 1965. Forest clearcutting slash disposal was carefully conducted, and all the surface litter and tree roots were removed from the clear-cut site before planting pure KPP. The secondary forests and KPP grew in a natural environment, and there was no human interference after reforestation. The seven forest types used for this study shared a similar history of the homogeneous substrate, topography, regional climate, and original vegetation. The basic characteristics of these forest types are summarized in Table 1.

2.2. Soil Gas Flux Measurements

Fluxes of CH4, CO2, and N2O were measured three times per month between 09:00 a.m. and 11:00 a.m. local time throughout the observation year using the static chamber-gas chromatography method. Stainless steel chambers of 50 × 50 × 50 cm (length × width × height) were fabricated and equipped with two battery-operated fans of 10 cm in diameter for the mixing of air during the measurements. Two holes (2 cm in diameter) were drilled on the top of each chamber and fitted with rubber stoppers for headspace sampling [18]. One hole held a digital thermometer probe to measure the temperature in the chamber, and the other hole was used to collect the gas in the chamber. The experimental design was a randomized complete block design, and three replicated chambers were placed randomly at each site [16]. Each site was approximately 0.06 ha (20 × 30 m).
During the gas collection process, the chambers were inserted into permanently-installed stainless-steel lidless understructure (0.5 × 0.5 × 0.2 m), which had been fixed into the soil at a depth of 10 cm (we conducted field investigation and removed any shrubs or herbs within the chambers of 13 days before the first gas collection). Newly grown herbs were removed one day before collecting GHGs. The channel on top of the stainless-steel understructure was filled with water to make the system air-tight before the measurements [18].
Gas samples were collected at time intervals of 0, 10, 20, and 30 min after the chamber was placed in the plot using 50-mL hypodermic needles with three-way stopcocks. After being drawn, the gas samples were injected into pre-evacuated packs and brought to the laboratory for analysis to be conducted within one week using a gas chromatograph (GC, Agilent 7890A, Agilent Co., Santa Clara, CA, USA). To improve data quality, three measurements were selected for linear regression [18]. Sample sets were accepted when the linear regression was r2 > 0.90 for all gases CH4, CO2, and N2O [28].

2.3. Determination of Environmental Variables

When gas samples were collected, the interior air temperatures of the chamber, atmospheric environment temperatures and soil temperatures at 5 cm were recorded with digital thermometers (JM624, Jinming instrument CO. LTD, Tianjin, China). Soil volumetric water content (cm3 H2O cm−3 soil) was also measured at 5 cm by a portable soil moisture tester (HS2, Campbell Scientific Inc, Logan, Utah, USA), which included a soil water sensor (MP-406B), a matched sampling connective rod, and a datalogger (MPM160 m, Campbell Scientific Inc, Logan, Utah, USA) [28].
A soil core, approximately 5 cm in diameter and 10 cm in length, was collected from each chamber location once per month during the monitoring year. Soil samples were transported on ice in an insulated cooler to the laboratory and stored in a refrigerator at +4 °C until analysis [13]. Soil samples were analyzed within 48 h. All the soil samples had three replicates. The soil core was sieved to 2 mm. Soil pH was determined by a PHS-3S pH meter in suspensions composed of a 1:5 ratio of soil to water. Soil N content (NO3- and NH4+) were extracted with a 2 M KCl solution (soil:solution, 1:5) and then filtered through a 0.45 μm filter [29]. The extracted solutions were measured by AA3 Continuous Flow Analytical System. Soil organic carbon was analyzed using a multi-N/C 2100 analyzer (Analytic Jena AG, Jena, Germany).

2.4. Global Warming Potential

The global warming potential of soils under seven different vegetation types in present study were calculated as follows [30].
GWP = CO 2 + N 2 O × 298 + CH 4 × 25 .
The cumulative GHG fluxes (GWP) = GHG fluxes (GWP) per unit area × areal extent.

2.5. GHG Flux Directions

It should be noted that throughout the paper, the movement of GHGs from soil to atmosphere is giving a positive sign, and from atmosphere to soil, it is giving a negative sign.

2.6. Data Analysis

The relationship between RS and T was examined by using an exponential model
RS = α × expβ×T,
where RS is the soil respiration (μmol CO2 m−2 s−1), T is the soil temperature at a depth of 5 cm, α and β are regression coefficients. Q10 is a quotient of change in RS caused by change in temperature by 10 °C, and can be indicative of the sensitivity of RS to temperature. It is calculated as follows [31]:
Q10 = exp10β.
All the experimental data were calculated by averaging the measurements of the three replicates for each sampling. One-way ANOVA (Duncan comparison) was conducted to examine the difference in CH4, CO2, and N2O fluxes among the seven forest types. Stepwise multiple linear regression was developed to quantify the response of the environmental variables to CH4, CO2, and N2O fluxes in the seven forest types. We used a Bonferroni correction of α = 0.05/7 = 0.0071 to adjust for multiple regression analysis, which is considered as “significant correlation” when it ranges from 0.0071 to 0.05. All the statistical analyses were carried out using SPSS version 17.0 software (SPSS, Inc. Chicago, IL, USA).

3. Results

3.1. Soil Properties

OMF and KPP had higher annual average air temperatures than those of the five secondary forests except for MOF, and OMF had a higher annual average soil temperature than that of the secondary forests by 0.30–3.03 °C and of KPP by 2.78 °C (p < 0.05, Table 2). The annual average soil moisture decreased in the following order: OMF > HWF > PDF > KPP > MDF > BPF > MOF (p < 0.05, Table 2). The soil pH of KPP was significantly higher than that of both OMF and the secondary forests (p < 0.05, Table 2). The concentrations of NO3-N and NH4+-N in KPP were significantly higher than those of OMF by 80.1% and 273.4%, and of secondary forests by 280.6%–795.4% and 385.3%–458.3%, respectively (p < 0.05, Table 2). The SOC in HWF soils was the highest among all the forest stands, and the SOC in OMF was significantly higher than that of KPP, PBF, and MOF by 30.3%, 38.6%, and 48.7%, respectively (p < 0.05, Table 2).

3.2. Seasonal Variation of GHG Fluxes

3.2.1. CH4 Fluxes

It was observed that the CH4 fluxes were negative with clear seasonal variations among the seven forest types. The experiment found that the highest CH4 absorption peak occurred in summer while the lowest absorption peak occurred in winter (Figure 2). The CH4 absorption from secondary forests was significantly higher than that of KPP by 32.6%–67.0% (p < 0.05) except for BPF, and it was recorded from MDF and MOF to be significantly higher than that of OMF and the other secondary forests during the sampling period (p < 0.05, Figure 2). Secondary forests averaged a higher CH4 absorption by 19.6%–51.0% compared with OMF except for BPF (Figure 2). There was a significant difference (p < 0.05) in CH4 absorption among the seven forest types during the non-growing season, whereas no significant difference was observed during the growing season (p > 0.05, Figure 2). The CH4 absorption from OMF, KPP, and the five secondary forests during the growing season were 2.5 times, 5.5 times, and 1.7–3.2 times higher than that of the non-growing season, respectively (Figure 2).
Stepwise multiple regression analysis for CH4 flux is as follows: in OMF, CH4 fluxes had significant negative correlations with NH4+-N (p < 0.05) and SOC (p < 0.0071). In KPP, CH4 fluxes had negative correlations with air temperature (p < 0.0071), and significant positive correlations with NO3-N (p < 0.05). For the five secondary forests, CH4 fluxes had significant positive correlations with air temperature, and negative correlations with soil temperature in HWF and MDF. In BPF, soil temperature, SVWC, and NO3-N were the dominant factors controlling CH4 fluxes (p < 0.0071). In PDF, CH4 fluxes had significant negative correlations with air temperature (p < 0.0071), and in MOF, no significant relationships were observed between those environmental factors and CH4 fluxes (p < 0.1) (Table 3).

3.2.2. CO2 Fluxes

The CO2 flux of each vegetation type was always positive with a clear seasonal variation. The highest emissions occurred in summer and the lowest emissions observed in winter for each site (Figure 3). The mean annual CO2 fluxes from the five secondary forests throughout the sampling period were significantly higher than that from OMF by 32.9%–78.6% (p < 0.05). Secondary forests averaged a higher CO2 flux by 14.1%–53.4% compared with KPP (Figure 3). For CO2 fluxes, there were significant differences (p < 0.05) among OMF, KPP, and secondary forests in different seasons (Figure 3). The CO2 emissions from OMF, KPP, and five secondary forests during the growing season were significantly higher than during the non-growing season by 1020.2%, 597.7%, and 382.6%–539.5%, respectively.
Stepwise multiple regression analysis showed that CO2 fluxes, as measured from KPP, OMF, and secondary forests, were significantly positively correlated with soil temperature (p < 0.0071) (Table 3). The exponential models of RS against T explained 70%, 92%, and 39%–82% of the seasonal variability in RS in OMF, KPP, and five secondary forests (Figure 4). The Q10 values (the sensitivity of RS to temperature) for RS were 2.20, 3.16, 2.59, 2.92, 2.75, 2.32, and 1.93 in OMF, KPP, HWF, BPF, PDF, MDF, and MOF, respectively (Figure 4). In addition, Table 3 shows that CO2 fluxes from OMF also had significant positive correlations with SVWC (p < 0.05). CO2 fluxes from KPP also had significant negative correlations with SVWC (p < 0.0071). CO2 fluxes from MDF also had significant negative correlations with SOC (p < 0.05). CO2 fluxes from MOF also had significant negative correlations with air temperature and NO3-N (p < 0.05) (Table 3).

3.2.3. N2O Fluxes

The N2O fluxes were always positive throughout the sampling period from OMF, KPP, and the five secondary forests. There were significant differences in N2O fluxes in each observation season among the experimental forest types (p < 0.05). The N2O fluxes in spring were significantly higher than those in the other seasons except OMF (p < 0.05, Figure 5). According to the annual statistics, the N2O fluxes decreased in the following order: MDF > OMF > KPP > PDF > HWF > MOF > BPF (p < 0.05). The N2O fluxes from PDF and MOF were significantly lower than that of OMF by 59.9% and 35.9%, and of KPP by 59.0% and 34.4%, respectively (p < 0.05, Figure 5). However, for N2O fluxes, there was no significant difference (p > 0.05) between OMF and KPP. The N2O emissions from KPP, PDF, and MOF during the non-growing season were significantly higher than those during the growing season (p < 0.05, Figure 5).
From the stepwise multiple regression analysis for N2O fluxes, in OMF, N2O fluxes had positive correlations with NO3-N (p < 0.0071). However, no significant relationships were observed between those environmental factors and N2O fluxes from KPP and BPF. N2O fluxes from HWF had significant positive correlations with air temperature and NH4+-N (p < 0.05). The soil pH and NH4+-N were the dominant factors controlling N2O fluxes from PDF (p < 0.05). In MDF, N2O fluxes had significant negative correlations with SVWC, pH, and NH4+-N, and a significant positive correlation with air temperature. A significant positive correlation was only observed between SVWC and N2O fluxes from MOF (Table 3).

3.3. Estimation of GHG Emissions and GWPs

CH4 fluxes varied from −4.12 (KPP) to −6.74 (MDF) kg CH4 ha−1 year−1 in different forest types (Figure 6). The cumulative CH4 fluxes varied from −9.48 (KPP) to −11,201.80 (HWF) kg CH4 y−1. CO2 fluxes varied from 12.46 (OMF) to 22.07 (MDF) t CO2 ha−1 year−1 in all the plots, and four secondary forest types (except for HWF) were significantly higher than that of KPP by 30.5%–50.1%, and five secondary forest types were 31.9%–77.1% greater than that of OMF (p < 0.05, Figure 6). The cumulative CO2 fluxes increased in the order of Korean pine plantation (33.80 t CO2 year−1) < over-mature forest (733.22 t CO2 year−1) < secondary forest (885.19–34,230.43 t CO2 year−1) (Table 4). They in the secondary forest were 55.7 and 1208.3 times higher than those in OMF and KPP, respectively. N2O fluxes varied from 0.62 (BPF) to 5.45 (OMF) kg N2O ha−1 y−1 in all the plots, and they in OMF were significantly higher than those in OMF and secondary forests by 228.3% and 201.1%–779.0% (p < 0.05, Figure 6). The cumulative N2O fluxes varied from 3.82 (KPP) to 2520.93 (HWF) kg N2O year−1. The total cumulative CH4, CO2, and N2O fluxes were –13.37 t CH4 year−1, 41,608.96 t CO2 year−1, and 3.24 t N2O year−1 through the whole year in seven forest types at the Maoershan Ecosystem Research Station in Northeast China (Table 4).
In the research presented in this paper, the GWP was computed based on the fluxes of CH4, CO2, and N2O measured from soils. The GWP of different secondary forests varied from 16.66 t CO2 eq. ha−1 (HWF) to 22.44 t CO2 eq. ha−1 (MDF) (Table 5). However, five secondary forests averaged higher GWP by 33.7%–80.1% compared with OMF. Secondary forests, except for HWF, averaged a higher GWP by 31.6%–52.8% compared with KPP. Additionally, no significant differences were observed between OMF and KPP (Table 5). One-way ANOVA analyses revealed that there was a remarkable difference among seven forest types (p < 0.05, Table 5). The cumulative GWP increased in the order of Korean pine plantation (33.79 t CO2 eq year−1) < over-mature forest (733.26 t CO2 eq year−1) < secondary forest (898.95–34,702.67 t CO2 eq year−1) (Table 5). They in the secondary forest were 56.4 and 1224.8 times higher than those in OMF and KPP, respectively. CO2 fluxes were the largest contributor for GWP in all land uses, while CH4 and N2O fluxes showed a minor contribution to GWP (Table 5). The total cumulative GWP was 42,151.87 t CO2 eq year−1 through the whole year in seven forest types at the Maoershan Ecosystem Research Station in Northeast China (Table 5).

4. Discussion

4.1. Factors Controlling GHG Fluxes

4.1.1. Methane

CH4 fluxes showed similar seasonal patterns in seven forest types (CH4 sink sites). This finding is in accordance with previous studies that the maximum CH4 absorption value primarily occurred in summer and little emission value was found in the early spring at the same location [32], because the greater soil water environment due to the thawing of frozen soil in spring favors the production of an anaerobic environment. The anaerobic environment is beneficial to the methanogens’ metabolic activities and leads to an increase in the production of CH4 in spring [33]. Since the soil temperature increased gradually and reached the highest point in summer, some studies suggested that rising soil temperatures facilitated the consumption of CH4 [16,34,35]. The CH4 absorption value decreased with the decreasing soil temperature in winter. Soil temperature is an important controller of CH4 consumption at temperatures between −5 and 10 °C [35]; 20 and 30 °C [36]; and between 20 and 30 °C [34], CH4 consumption was greatly reduced below −5 °C as the low temperature significantly reduced microbial activities. However, it had a minimum soil temperature of about −16.4 °C below the 5 cm depth in winter in the present study, and thus the low temperature in winter is considered one of the factors for inducing low CH4 absorption in forest soils. Furthermore, the snow cover and frozen soil in winter could indicate a decrease in the soil pore connectivity, which hinders the diffusion of CH4 into the soil and leads to a lower uptake rate [13].
In this study, The CH4 fluxes showed significant negative correlations with the air temperature (p < 0.05), the soil temperature (p < 0.05), and SVWC (p < 0.05) (Table 3). The results suggested that adequate soil moisture and high soil temperature are necessary to support the aerobic CH4 consumption through the activity of methanotrophs in the soil [37]. In general, soil temperature and SVWC were the key factors that affected the activities of methanogens and methanotrophs as well as the permeability of atmospheric O2 into the soils, which was closely related to the interplay between CH4 production and consumption by oxidizing microorganisms [5,19]. The soil NO3-N and NH4+-N content presented a primary effect on CH4 fluxes in OMF, KPP, and BPF plots (Table 3). However, some studies found that the concentrations of NO3-N and NH4+-N could reduce CH4 fluxes while others reported opposite results [38,39]. This is because the interactions between the N and CH4 cycle are complex and therefore involve various uncertainties [40]. In the present study, CH4 from OMF exhibited a distinct negative correlation with SOC (Table 3). This is consistent with the previous conclusion that increased SOC continuously promotes CH4 oxidation [19]. In addition, soil physico-chemical properties indicated significant differences due to different forest types (Table 2), and stepwise multiple linear regression showed differences in CH4 fluxes among different forest types depending on the comprehensive influence of the above factors.

4.1.2. Carbon Dioxide

It was observed that the CO2 emissions were higher in summer as compared to winter during the monitoring of the seven vegetation types at the Maoershan Ecosystem Research Station in Northeast China (Figure 3). Similar results were found by Dou et al. [16]. The Q10 values for Soil respiration ranged from 1.93 to 3.16 across the seven forest ecosystems (Figure 4) and were consistent with the values previously reported for temperate forests (1.8–4.1) [41]. The Q10 values showed the varied response of soil respiration to temperature among the seven forest stands. Additionally, soil respiration was controlled by soil temperature, and there was a positive correlation between them (Table 3). These results suggest that the proper soil temperature could promote the increase of microbial activities and then easily enhance mineralization of unavailable decomposable organic substrates [5], and there is a high rate of decomposition of soil organic C at high temperatures [42,43]. A significant negative correlation was discovered between CO2 fluxes and the soil NO3-N in MOF (Table 3). The soil NO3-N can lead to carbon limitation, which has adverse effects on the nutrient supply of microorganisms. Moreover, soil nitrogen can reduce heterotrophic respiration by inhibiting decomposition of organic matter [44,45]. Additionally, there was a negative correlation between soil respiration rate and SOC in MDF. This is contrary to the finding that there was a significant positive correlation between soil respiration rate and soil organic C content in the forest system [46], which is because the soil organic C in the present study region was unable to provide adequate nutrition for soil organisms, leading to reduced biological activities and a slower respiration rate.

4.1.3. Nitrous Oxide

The N2O fluxes were negligible in the seven forest soils, and the highest fluxes occurred in the spring; some peaks of N2O emissions were observed in forest soils in the summer after rainfall (Figure 5). These results are aligned with other studies that showed low N2O emission in the forest soils and an increase in N2O emission rates after heavy rainfall [13,43]. This is also inconsistent with another study that showed the lowest N2O flux appeared in the hot season while the peak N2O flux appeared in the cold season in the afforested soils [16].
The thawing of the soil in the spring and heavy rainfall in the summer create slightly anaerobic conditions that lead to the emission of short-lived and intense N2O fluxes due to the denitrification process in some anaerobic microsites in the soil [47]. The increasing soil temperatures could increase both nitrification [48,49] and denitrification [49]. In drier areas where moisture is limited, soil mineralization and nitrification could be stimulated by increased moisture contents [50]. However, in wetter areas, increased moisture contents may induce anaerobiosis, allowing N2O production via denitrification [51]. Additionally, autotrophic nitrification could be inhibited by low soil pH [52], and denitrification rates are also generally decreased by acidity [52]. In OMF, abundant NO3-N provided sufficient substrates for denitrification. The increased concentrations of NH4+ following harvest can stimulate nitrification and NO3 production [53]. The N2O emissions were negatively correlated with NH4+-N (p < 0.05) in PDF. The NH4+ requirement of nitrifying microorganisms varies with the change of NH4+ concentration. Therefore, the response of nitrification process is different at the concentration change of NH4+ [54], which implies that NH4+-N inhibits microbes via nitrification and N2O emissions.

4.2. Effects of Different Forest Types on GHG Fluxes

Land use change is one of the most important factors that affect global warming. It affects the structure and function of ecosystem, changes the basic characteristic parameters of the soil surface, and influences the change of chemical composition in the atmosphere through the biogeochemical cycle, leading to the climate change [11,16,23]. In the present study, GHG fluxes varied considerably among the seven forest types, suggesting the influence of the forest conversion on the rate of transport and emission of GHGs.
It was found that forest conversion significantly affects CH4 absorption capacity in the present study (Figure 2). This finding is consistent with the previous conclusion that deforestation increased soil CH4 absorption in various soils [1,55]. Deforestation is usually accompanied by considerable changes in soil properties (e.g., soil texture, soil C and N substrate availability), which consequently control soil CH4 production and oxidation [56]. The shift in methanotrophic type is one of the factors that lead to a change in soil CH4 oxidation [55], because the land use change greatly influences the methanotrophic community structure and activity [57]. In addition, it is also related to the plant allelopathy; there is a large group of monoterpene species in Pinaceae plants, which decompose into the soil through the roots and litter and have an inhibitory effect on CH4 oxidation [36]. Maurer et al. [58] also found that the toxic compounds produced by conifer species could impede soil CH4 oxidation. Thus, in the present research, it is inferred that KPP (conifer species) in the Maoershan region may limit soil CH4 oxidation in the methanotroph community structure and accelerate CH4 micro-emission.
In this study, different environmental factors, dependent on the forest stands, influenced the soil respiration in different ways. By the harvesting in the early growth stages of secondary forests and KPP, forest lost the canopy cover; water and heat balance changed fundamentally due to the sudden disappearance of the canopy. The intensifying sunlight would lead to the increase of soil surface evaporation and the acceleration of litter decomposition. The disturbance (movement, scratch, and compaction, etc.) of topsoil caused by logging leads to certain changes in soil microenvironment, microbial population structure, soil physical and chemical properties, such as soil porosity, soil aggregate structure, and soil water-stable aggregate structure [11]. Therefore, the soil under different trees possesses diverse soil C stability and hydrological conditions and hence has different degrees of influences on soil respiration [59,60]. SVWC in HWF was relatively higher than that in other secondary forest soils, and the CO2 fluxes were lower than those from other secondary forests and KPP (Table 2 and Figure 2). This is caused by the soil water status, such as the soil with high water content in HWF, the poor soil permeability hindered the diffusive exchange of CO2 between the soil and the atmosphere [37]. Consequently, differences in CO2 fluxes among forests in our study suggest different environmental conditions among the seven forest types.
N2O fluxes from forest soils are a complex process, which is not only controlled by environmental factors such as soil temperature and moisture [28], but also affected by factors such as N and C availability in soil, vegetation biomass [61], and microbial community dynamics. Furthermore, vegetation types can contribute differently to these factors [62]. Plant species differ in their ability to influence nitrification and denitrification processes. Plants can also affect the N2O emission by modifying NH4+, NO3, and C contents and by changing the partial O2 pressure in the soil [63].
The total GWP was significantly higher in the secondary forest than that in OMF and KPP (Table 5). The GWP mainly consisted of more than 90% CO2 flux, and the CO2 flux was significantly higher in the secondary forest than that in OMF and KPP (Figure 6). This is within the range of 84%–99% contribution from CO2 emission by Mu et al. [64]. Therefore, higher total GWP resulted from higher CO2 flux in the secondary forest than that in OMF and KPP. Although N2O flux was significantly higher in OMF than that in secondary forest and KPP (Figure 6), the N2O flux accounted for 13.1% of GWP. This is within the range of 4% (agriculture) to 16% (undisturbed forest) from N2O on GWP observed by Shrestha et al. [65]. Thus, the influence of N2O flux was minor for GWP. Similar results were reported in other studies that CO2 was the major contributor for GWP [66,67,68]. The GWP comparison of the seven forest types indicates that the effects of the early human activities have not been completely eliminated in the middle stage of KPP and secondary forests.

5. Conclusions

Greenhouse gas fluxes evaluated for forest conversion (the over-mature forests, Korean pine plantation, and five secondary forests) indicated that CH4 uptake in MDF and MOF was significantly higher than that in OMF. Secondary forests averaged a higher CO2 flux by 32.9%–78.6% compared with OMF. The N2O flux in BPF and MOF was lower than that in OMF. There was no significant difference in GHG fluxes between KPP and OMF. The above conclusions show that GHG fluxes in several 60-year-old secondary forest types after clearcutting have not recovered to the level of OMF, which requires continuous monitoring for a long time. The GWP in KPP is significantly less than the paddy field, orchard, and upland GWP, and it in the secondary forest it is less than the paddy field, orchard GWP [69], suggesting that forest is a more desirable land use in reducing global warming.
However, based on the GWP comparison of seven forest types, GHG fluxes from the soil, without taking into account the GHG absorption by plants, has led to the increase of GWP in the study area due to the forest conversion, which has caused the regional climate warming. Air and soil temperature, SVWC, pH, NO3-N, NH4+-N, and SOC are the most influential variables affecting GHG fluxes among soil properties studied, which, if manipulated, can help in mitigating GHG emissions and GWP. These findings suggest that forest conversion has important effects on GHG emissions and GWP.
Further studies are required for the comparison of the GHG fluxes and GWP between over-mature forest, secondary forests, plantations (Mongolian pine plantation, Larch plantation, and so forth), and croplands to forecast which vegetation type is most similar to the over-mature forest in GHG emissions, and to estimate the GHG emissions and GWP of the whole study area.

Author Contributions

C.M. and B.W. conceived the general idea of the paper, supervised and conducted the laboratory and/or field observations and measurements. All authors analyzed and discussed the results; B.W. and C.M. wrote and revised the paper; C.M. reviewed the edited manuscript.

Funding

The authors wish to acknowledge financial assistance supported by the National Natural Science Foundation of China (41430639), and the National Key Research and Development Program of China (2017YFC0504102).

Acknowledgments

We would like to thank the Maoershan Ecosystem Research Station for the field experiment. We also would like to thank the reviewers and editors for their time and effort.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Map of the study site at the Maoershan Ecosystem Research Station of Northeast Forestry University in Northeast China (right map). The study site is located at the yellow pentacle of the left map. Seven black dots in the right map represent sampling sites of seven forest types. Abbreviation: OFT: other forest types (outside the seven forest types in present paper).
Figure 1. Map of the study site at the Maoershan Ecosystem Research Station of Northeast Forestry University in Northeast China (right map). The study site is located at the yellow pentacle of the left map. Seven black dots in the right map represent sampling sites of seven forest types. Abbreviation: OFT: other forest types (outside the seven forest types in present paper).
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Figure 2. Seasonal variations (a), dynamics of Gs and Ngs (b), and the annual average (b) of methane fluxes from the over-mature forest, Korean pine plantation, and five secondary forests at the Maoershan Ecosystem Research Station in Northeast China. The lowercase letters indicate statistically significant differences within treatments among different seasons. The capital letters indicate statistically significant differences within seasons among different treatments. Abbreviations: Spr., Spring; Sum., Summer; Aut., Autumn; Win., Winter; Gs, Growing Season; Ngs, Non-growing Season; Aa, Annual Average.
Figure 2. Seasonal variations (a), dynamics of Gs and Ngs (b), and the annual average (b) of methane fluxes from the over-mature forest, Korean pine plantation, and five secondary forests at the Maoershan Ecosystem Research Station in Northeast China. The lowercase letters indicate statistically significant differences within treatments among different seasons. The capital letters indicate statistically significant differences within seasons among different treatments. Abbreviations: Spr., Spring; Sum., Summer; Aut., Autumn; Win., Winter; Gs, Growing Season; Ngs, Non-growing Season; Aa, Annual Average.
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Figure 3. Seasonal variations (a), dynamics of Gs and Ngs (b), and the annual average (b) of carbon dioxide emissions from the over-mature forest, Korean pine plantation, and five secondary forests at the Maoershan Ecosystem Research Station in Northeast China. See Figure 2 for the explanation of the different lowercase letters and capital letters and the different abbreviations.
Figure 3. Seasonal variations (a), dynamics of Gs and Ngs (b), and the annual average (b) of carbon dioxide emissions from the over-mature forest, Korean pine plantation, and five secondary forests at the Maoershan Ecosystem Research Station in Northeast China. See Figure 2 for the explanation of the different lowercase letters and capital letters and the different abbreviations.
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Figure 4. Relationships between soil respiration and soil temperature at a depth of 5 cm (a) and Q10 (b) for the seven forest stands.
Figure 4. Relationships between soil respiration and soil temperature at a depth of 5 cm (a) and Q10 (b) for the seven forest stands.
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Figure 5. Seasonal variations (a), dynamics of Gs and Ngs (b), and the annual average (b) of nitrous oxide emissions from the over-mature forest, Korean pine plantation, and five secondary forests at the Maoershan Ecosystem Research Station in Northeast China. See Figure 2 for the explanation of the different lowercase letters and capital letters and the different abbreviations.
Figure 5. Seasonal variations (a), dynamics of Gs and Ngs (b), and the annual average (b) of nitrous oxide emissions from the over-mature forest, Korean pine plantation, and five secondary forests at the Maoershan Ecosystem Research Station in Northeast China. See Figure 2 for the explanation of the different lowercase letters and capital letters and the different abbreviations.
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Figure 6. CH4 fluxes (a), CO2 fluxes (b), and N2O fluxes (c) from the over-mature forest, Korean pine plantation and five secondary forests at the Maoershan Ecosystem Research Station in Northeast China. The lowercase letters indicate statistically significant differences among different treatments.
Figure 6. CH4 fluxes (a), CO2 fluxes (b), and N2O fluxes (c) from the over-mature forest, Korean pine plantation and five secondary forests at the Maoershan Ecosystem Research Station in Northeast China. The lowercase letters indicate statistically significant differences among different treatments.
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Table 1. Characteristics of stands from which sample trees were harvested a.
Table 1. Characteristics of stands from which sample trees were harvested a.
Forest
Type
Age
(Year)
Main Species Composition b
(Dominant Species)
Land-Use HistoryAreal Extent
(ha)
Density
(Trees ha–1)
BA
(m2 ha–1)
DBH (cm)
MeanRange
OMF>160(8), 9, 2, 3, 10, 11, 12, 13, 14Natural forest58.8778954.625.34.0–69.1
KPP51(8), 6, 3, 9, 5, 7, 6Reforestation in 19652.3211142.115.03.9–34.4
HWF60(4), (3), 2, (7)Natural regeneration after clearcutting in 19552083.41157826.210.11.6–50.3
BPF65(6), 15, 12, 17, 18Natural regeneration after clearcutting in 195080.7302225.47.41.4–34.8
PDF65(6), (5), 7, 4, 3, 2Natural regeneration after clearcutting in 195044.4183334.112.11.6–49.4
MDF652, 7, 4, 3, 5, 6Natural regeneration after clearcutting in 195099.47202234.811.31.9–49.6
MOF66(1), 2, 3, 4, 5, 6Natural regeneration after clearcutting in 1949101.06151136.613.72.0–55.4
a Note: BA stands for the basal area; DBH for diameter at breast height; OMF for over-mature forest; KPP for Korean pine plantation; HWF for hardwood forest; BPF for Betula platyphylla forest; PDF for Populus davidiana forest; MDF for mixed deciduous forest; MOF for Mongolian oak forest. HWF, BPF, PDF, MDF, and MOF were five major secondary forests. b 1, Quercus mongolica Fisch.; 2, Tilia amurensis Rupr.; 3, Fraxinus mandshurica Rupr.; 4, Phellodendron amurense Rupr.; 5, Populus davidiana Dode.; 6, Betula platyphylla Suk.; 7, Juglans mandshurica Maxim.; 8, Pinus koraiensis Sieb. et Zucc.; 9, Betula costata Trautv. 10, Acer ukurunduense Trautv. et Mey; 11, Abies nephrolepis (Trautv.) Maxim; 12, Ulmus laciniata (Trautv.) Mayr; 13, Acer tegmentosum Maxim; 14, Acer mono Maxim; 15, Larix gmelinii Rupr.; 16, Picea koraiensis Nakai; 17, Ulmus japonica (Rehd.) Sarg; 18, Alnus sibirica Fisch. ex Turcz.
Table 2. Physico-chemical properties of experimental sites. Data are expressed as mean ± SE, n = 12.
Table 2. Physico-chemical properties of experimental sites. Data are expressed as mean ± SE, n = 12.
PropertiesOMFKPPHWFBPFPDFMDFMOF
Air T (°C)5.92 ± 0.01B5.63 ± 0.01B3.44 ± 0.16E4.79 ± 0.02C4.05 ± 0.21D4.93 ± 0.05C6.93 ± 0.05A
Soil T (°C)7.20 ± 0.01A4.42 ± 0.01E4.17 ± 0.06F5.24 ± 0.07D5.63 ± 0.04C5.17 ± 0.15D6.90 ± 0.11B
Soil moisture (%)35.45 ± 0.49A27.90 ± 0.12C34.42 ± 0.33A26.58 ± 0.32D30.84 ± 0.41B27.35 ± 0.56D23.96 ± 0.47E
Soil pH5.51 ± 0.04C6.02 ± 0.03A5.66 ± 0.16B5.43 ± 0.05BC5.47 ± 0.24BC5.53 ± 0.06B5.72 ± 0.05B
NO3-N (mg kg−1)5.42 ± 0.35B9.76 ± 0.19A2.58 ± 0.26C1.09 ± 0.13D2.22 ± 0.42C2.18 ± 0.35C1.98 ± 0.20C
NH4+-N (mg kg−1)6.46 ± 0.48B24.12 ± 2.32A4.97 ± 0.07B4.95 ± 0.66B4.60 ± 1.69B4.38 ± 0.75B4.32 ± 0.19B
Soil organic C (g kg−1)72.02 ± 7.25B55.26 ± 1.08CD96.37 ± 8.15A51.96 ± 3.32D61.77 ± 2.07BCD69.92 ± 4.52BC48.42 ± 2.37D
Note: The capital letters after numerical values indicate statistically significant differences within physico-chemical properties under different vegetation types (p < 0.05).
Table 3. The driving factors of CH4, CO2, and N2O fluxes in seven types of vegetation at the Maoershan Ecosystem Research Station in Northeast China.
Table 3. The driving factors of CH4, CO2, and N2O fluxes in seven types of vegetation at the Maoershan Ecosystem Research Station in Northeast China.
PlotATSTSVWCpHNO3-NNH4+-NSOCInterceptR2p
Models for soil CH4
OMF −0.008 *−0.005 **0.587 *0.663<0.05
KPP−0.002 ** 0.004 * −0.129 *0.842<0.0071
HWF0.003 *−0.007 ** −0.045 **0.612<0.05
BPF −0.004 **0.002 * 0.198 ** −0.117 *0.927<0.0071
PDF−0.003 ** −0.047 **0.626<0.0071
MDF0.004 *−0.007 * −0.057 **0.441<0.1
MOF 0.8360.330<0.1
Models for soil CO2
OMF 12.920 **15.382 * −512.687 *0.891<0.0071
KPP 21.477 **−6.017 ** −476.7290.973<0.0071
HWF 16.692 ** 114.102 **0.833<0.0071
BPF 23.067 ** 57.177 *0.895<0.0071
PDF 25.094 ** 2928.966 *0.944<0.0071
MDF 33.999 ** −10.888 *454.8560.936<0.0071
MOF−19.018 *49.746 ** −112.542 * 388.194 **0.943<0.0071
Models for soil N2O
OMF 0.018 ** −0.068 *0.851<0.0071
KPP 0.016 *0.018nsc
HWF0.01 * 0.009 * −0.0400.537<0.05
BPF 0.0050.284<0.1
PDF −0.121 * −0.058 * 0.891 *0.640<0.05
MDF0.001 ** −0.002 *−0.155 ** −0.018 * 0.994 **0.797<0.05
MOF 0.001 * −0.0050.494<0.05
Note: * indicates significant effects at p < 0.05; ** indicates significant effects at p < 0.0071; nsc indicates no significant correlation. AT, air temperature; ST, soil temperature.
Table 4. Estimation of the cumulative GHG fluxes for each forest type at the Maoershan Ecosystem Research Station in Northeast China. Data are expressed as mean ± SE, n = 3.
Table 4. Estimation of the cumulative GHG fluxes for each forest type at the Maoershan Ecosystem Research Station in Northeast China. Data are expressed as mean ± SE, n = 3.
SitesTotal CH4 Flux (kg CH4 year−1)Total CO2 Flux (t CO2 year−1)Total N2O Flux (kg N2O year−1)
OMF−284.84 ± 18.32a (2.13%)733.22 ± 105.79de (1.76%)321.01 ± 22.60b (9.90%)
KPP−9.48 ± 0.30a (0.07%)33.80 ± 0.76e (0.08%)3.82 ± 0.13b (0.12%)
HWF−11,201.8 ± 774.17b (83.76%)34,230.43 ± 915.36a (82.27%)2520.93 ± 358.65a (77.75%)
BPF−354.00 ± 13.36a (2.65%)1591.67 ± 15.97bcd (3.83%)50.57 ± 7.13b (1.56%)
PDF−234.28 ± 32.66a (1.75%)885.19 ± 60.78cde (2.13%)65.86 ± 2.63b (2.03%)
MDF−670.43 ± 34.61a (5.01%)2194.97 ± 126.88b (5.28%)180.37 ± 3.27b (5.56%)
MOF−619.50 ± 50.34a (4.63%)1939.68 ± 83.08bc (4.66%)99.71 ± 10.04b (3.08%)
Total flux−13,374.33 ± 841.9041,608.96 ± 814.403242.27 ± 336.40
Note: The different lowercase letters after numerical values indicate statistically significant differences within GHG fluxes (CH4, CO2, and N2O) under different forest types.
Table 5. Estimation of Global Warming Potential (GWP) for each forest type at the Maoershan Ecosystem Research Station in Northeast China. Data are expressed as mean ± SE, n = 3.
Table 5. Estimation of Global Warming Potential (GWP) for each forest type at the Maoershan Ecosystem Research Station in Northeast China. Data are expressed as mean ± SE, n = 3.
SitesGWPCH4
(t CO2 eq. ha−1 year−1)
GWPCO2
(t CO2 eq. ha−1 year−1)
GWPN2O
(t CO2 eq. ha−1 year−1)
Total GWP
(t CO2 eq. ha−1 year−1)
Cumulative GWP
(t CO2 eq year−1)
OMF−0.12 ± 0.01ab12.46 ± 1.80d1.63 ± 0.11a12.46 ± 1.80d 733.26 ± 105.80cd (1.74%)
KPP−0.10 ± 0.01a14.70 ± 0.33cd0.49 ± 0.02bc14.69 ± 0.33cd33.79 ± 0.76d (0.08%)
HWF−0.14 ± 0.01abc16.43 ± 0.44bc0.36 ± 0.05cd16.66 ± 0.38bc34,702.67 ± 794.28a (82.33%)
BPF−0.11 ± 0.01a19.72 ± 0.35ab0.19 ± 0.03e19.80 ± 0.22ab1597.59 ± 17.90bc (3.79%)
PDF−0.13 ± 0.02abc19.94 ± 1.37a0.44 ± 0.02bcd20.25 ± 1.40a898.95 ± 61.95cd (2.13%)
MDF−0.17 ± 0.01c22.07 ± 1.28a0.54 ± 0.01b22.44 ± 1.28a2231.78 ± 127.62b (5.29%)
MOF−0.15 ± 0.01bc19.19 ± 0.82ab0.29 ± 0.03de19.33 ± 0.80ab1953.83 ± 79.86b (4.64%)
Total 42,151.87 ± 698.53
Note: The different lowercase letters after numerical values indicate statistically significant differences within GWP under different forest types.

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